Adaptive Multi-Histogram Equalization using Human Vision Thresholding

Transcription

1 Adaptive Multi-Histogram Equalization using Human Vision Thresholding Eric Wharton a, Karen Panetta a, Sos Agaian b a Tufts University, 6 College Avenue, Medford, MA 055 USA b The University of Texas at San Antonio, 604 West, San Antonio, TX 7849 USA ABSTRACT mage enhancement is the task of applying certain alterations to an input image such as to obtain a more visually pleasing image. The alteration usually requires interpretation and feedback from a human evaluator of the output resulting image. Therefore, image enhancement is considered a difficult task hen attempting to automate the analysis process and eliminate the human intervention. Furthermore, images that do not have uniform brightness pose a challenging problem for image enhancement systems. Different kinds of histogram equalization techniques have been employed for enhancing images that have overall improper illumination or are over/under exposed. Hoever, these techniques perform poorly for images that contain various regions of improper illumination or improper exposure. n this paper, e introduce ne human vision model based automatic image enhancement techniques, multi-histogram equalization as ell as local and adaptive algorithms. These enhancement algorithms address the previously mentioned shortcomings. We present a comparison of our results against many current local and adaptive histogram equalization methods. Computer simulations are presented shoing that the proposed algorithms outperform the other algorithms in to important areas. First, they have better performance, both in terms of subjective and objective evaluations, then that currently used algorithms on a series of poorly illuminated images as ell as images ith uniform and non-uniform illumination, and images ith improper exposure. Second, they better adapt to local features in an image, in comparison to histogram equalization methods hich treat the images globally. Keyords: Histogram Equalization, Human Visual System, mage Enhancement, Performance Measure, Logarithmic mage Processing, Adaptive Multi-Histogram Equalization. NTRODUCTON Producing digital images ith good brightness/contrast and detail is a strong requirement in several areas including vision, remote sensing, biomedical image analysis, and fault detection. Producing visually natural images or transforming the image such as to enhance the visual information ithin is a primary requirement for almost all vision and image processing tasks. Methods that implement such transformations are called image enhancement techniques [9][0]. The task of image enhancement is a difficult one considering the fact that there is no general unifying theory of image enhancement at present, because there is no general standard of image quality that can serve as a design criterion for an image enhancement processor [8][]. mages that do not have uniform brightness pose a challenging problem for image enhancement systems. Histogram equalization and its variations have traditionally been used to correct for uniform lighting and exposure problems. These variant algorithms are outlined in [][][3][4][5]. Hoever, as stated by Chen in [], there are still cases that are not handled ell by these more advanced algorithms. For the more difficult problem of non-uniform lighting or exposure, simple histogram equalization tends to increase these effects, and bi-histogram equalization typically ill enhance either the properly illuminated or improperly illuminated portions, destroying the information in the other portion of the image. Adaptive histogram equalization methods, as is stated in [5], can cause excessive noise amplification in regions ith a small intensity range, a feature common to improper exposure or illumination. Hoever, this method performs poorly for images that contain various regions of improper illumination or improper exposure. n addition, all of these methods have been knon to introduce artifacts into the images [][3][4][5]. n summary, the enhancement methods most idely employed treat the spatial information in the image in a global

2 fashion, hile in many cases it is necessary to adapt the transformation to the local features ithin different regions of the image [6]. Automating this enhancement process, that is creating a method to yield enhanced images ithout human (subjective) intervention, is a notoriously difficult task in image processing [7,8]. n this paper, e introduce the human vision system based adaptive multi-histogram equalization method to address these shortcomings. Despite the improved performance of bi-histogram equalization over standard histogram equalization, extending to tri-histogram equalization using the same methods does not yield better results. Comparing the results of tri-histogram equalization to bi-histogram equalization on many images shoed no consistent improvement from bi-histogram equalization to tri-histogram equalization. Another method is necessary to achieve better results using multi-histogram equalization. By separating the image into regions by the quality of illumination, such as over-illuminated, ell illuminated, and under-illuminated, traditional histogram equalization can be used on each region to correct for non-uniform illumination. n order to perform this segmentation of the image, e utilize a common model of the human visual system. Human visual response can be characterized by the amount of contrast required for a human observer to sense a change. This is modeled by the ell knon Weber Contrast La for the case of a properly illuminated scene. Hoever, human visual response is not the same for any amount of illumination. Practically, the human visual response can be divided into three regions. The region of visual response that a pixel is mapped to is based upon the background intensity. For this reason, instead of thresholding the image based upon the pixel intensities themselves, as in bi-histogram equalization, e segment the image based upon the background intensity. This allos for a more accurate segmentation into the three regions of human visual response, solving the problems encountered ith standard tri-histogram equalization. n this paper, e introduce the multi-histogram equalization method, as ell as local and adaptive multi-histogram equalization algorithms. These algorithms ill be demonstrated on a varied collection of ell and poorly illuminated images, as ell as images that sho uniform and non-uniform illumination. We ill also demonstrate out results on images ith shados. These images ill include images captured using high quality professional digitization methods as ell as lesser quality images captured using cell phone cameras. We ill compare the results against the results of the other adaptive and local histogram equalization methods. The computer simulations generated ill sho the performance and advantages over other methods. This ill demonstrate the effectiveness of adaptive multi-histogram equalization for poorly illuminated and non-uniformly illuminated images. To further improve the performance of multi-histogram equalization, non-linear arithmetic using the Logarithmic mage Processing (LP) model is used to replace the classical arithmetic. t has been demonstrated that this model satisfies the four fundamental requirements for an image processing frameork; it is based on a physically relevant image formation model, the mathematical operations are consistent ith the physical nature of images, the operations are computationally effective, and it is practically fruitful, ie. t must serve some useful purpose [6]. This helps to achieve better results across a variety of images ith the multi-histogram equalization method. This paper is organized as follos: Section presents necessary background information, including a description of the standard histogram equalization method, the Logarithmic mage Processing (LP) model, and the Logarithmic AME performance measure. Section 3 ill present the Human Visual System based mage Enhancement (HVS) method and the Multi-Histogram Equalization algorithm using HVS. Section 4 ill present the results of computer simulations. Section 5 ill be a discussion of results and some concluding comments are made.. BACKGROUND NFORMATON n this section, e provide a brief description of the Logarithmic mage Processing (LP) model, the Logarithmic AME measure of enhancement performance, and the histogram equalization algorithm... Logarithmic mage Processing (LP) model The LP model as introduced by Jourlin and Pinoli to address the four fundamental requirements of an image processing frameork [6]. First, the frameork must be based on a physically relevant image formation model. Second, the mathematical operations must be consistent ith the physical nature of images. Third the operations must

3 be computationally effective. Finally, it must be practically fruitful; it must be shon that not only is the image processing model mathematically ell defined, it must serve some useful purpose. n effect, the LP model more accurately processes images and it has been shon that better results can be obtained in a variety of image processing applications by orking ithin this frameork [6][0][][][3][4]. The LP model as introduced to address to issues ith linear processing. First, linear arithmetic operations can return pixel values outside of the range [0,M), hich are generally clipped. This causes a loss of information. Second, linear operations typically do not yield results consistent ith the physical phenomena. The LP model is designed to both maintain the pixel values inside the range [0,M) as ell as to more accurately process images from a human visual system point of vie. n the LP model, images are processed as a collection of light absorption filters. An image is represented by a single light absorption filter, ith a certain thickness at any point. The image is seen by shining a uniform light source on the filter, and seeing the resulting image on a blank screen. To add images, light filters are placed in parallel. To subtract images, filters are removed. The image formation model is demonstrated in figure.. Uniform Source ncident Light Absorption Transmitted Light Screen shoing ntensity, i (i,j) Filter, g(i,j) ntensity, t (i,j) image, f(i,j) Figure.: Demonstrating the LP image formation model; and image, f( i, j ), and it s absorption filter, g( i, j ) The LP model can be summarized as follos: a b a b a ab M M ab M b g () () c (3) f a M M c M a b ( a) ( b) (4) here e use as LP addition, as LP subtraction, as LP scalar multiplication, and as LP grayscale multiplication. Also, a and b are any grey tone pixel values, M is the maximum value of the range, and c is a constant. n general, a and b correspond to the same pixel in to different images that are being added, subtracted, or multiplied. For the grayscale multiplication, the functions and are defined as: f a) M ln ln M ( (5) ( a) M M e f M e Where and are user-defined operating parameters hich can be fine-tuned for the specific images being processed. (6)

4 .. Measure of Enhancement A problem of image enhancement has alays been to develop a quantitative measure to assess image enhancement. Hoever, defining a good measure of enhancement is a daunting task, mainly because image quality can be a very subjective assessment. Also, a lack of a-priori knoledge of the optimal enhanced image further complicates this task. For example, hen using mean squared error measurements to determine optimal values, the original and ne images are knon and can be compared directly. For applications involving a measure of image enhancement, the optimal enhanced image is not knon, and cannot be used for comparison purposes. With the introduction of an effective measure of image enhancement, hoever, the measure could be used for automated selection of parameters making more complex image enhancement algorithms practical for industry and consumer applications. There is no universal definition of best hen it comes to image enhancement, hoever several measures have been defined that use the contrast of the image to measure enhancement [7][8]. Weber s and Fisher s las of the human visual system are a common starting point for most of these measures. Also note the three characteristics of a measure of image enhancement [9]. The Logarithmic AME and Logarithmic AMEE measures of image enhancement use Fisher s la and the entropy concept, respectively [0]. Both measures also make use of the Michelson contrast to quantify image contrast. The measures function by first segmenting an image into k k sized blocks, assessing each indo separately, and averaging the results. The measures use the folloing formulas:, k k max:, min:, log k l k l kk ( ) ln k k i j 0 max: k, l min: k, l (7) k k max:, min:, max:, min:, log l l l ( ) ln l k k k k i j max: k, l min: k, l max: k, l min: k, l (8), Where max; k l and min; k l are the local maximum and minimum, respectively. The summations use LP arithmetic. To use measure based selection of optimal parameters, an image is enhanced using the range of enhancement parameters. For each enhanced image, the image is assessed using the measures. This information is then organized into a graph, and the best images are located at the local extrema. t is also important to note that this data is relative; higher numbers are not necessarily better. This is image and method specific..3. Histogram Equalization Histogram equalization and its variations have traditionally been used to correct for uniform lighting and exposure problems ith good results. These variant algorithms are outlined in [][][3][4][5]. Standard histogram equalization is a global algorithm, meaning that it uses the same formula to process ever pixel in the image. t uses a cumulative density function to attempt to force a uniform probability density function for an image. As entropy is maximized hen there is a uniform distribution, this procedure should maximize the information in the image. Global histogram equalization functions by remapping the pixel intensities values according to the folloing transfer function: f ( min max min x) Y ( Y Y ) P( x) (9) Where f (x) is the pixel intensity in the output image, x is the pixel intensity in the input image, Y min and Ymax are the desired minimum and maximum of the output range, respectively, and P (x) is the probability distribution, here P ( X max ). The objective of this algorithm, to force a flat probability density, comes from the knoledge that entropy is maximized hen all pixel intensities are equally likely. Standard histogram equalization, hoever, has no parameters and cannot be adjusted to perform better ith different images. These issues have been ell documented, and many algorithms have been developed to either apply histogram

5 equalization on a smaller scale to regions of the image or to cause the algorithm to adapt to local image statistics in order to achieve better avoid adding image artifacts. These issues and modifications are outlined in [][][3][4][5]. One of these methods is bi-histogram equalization, hich decomposes an image into to sub-images and equalizes both separately. Many methods have been proposed to select the threshold for bi-histogram equalization. n [0] the measures of image enhancement are used to select optimal threshold values. n figure., e compare resulting images using the various methods of selecting parameters. As can be seen, this results in more visually pleasing enhanced images, as ell as more consistent results. This demonstrates the strength of the measure to select optimal image enhancement parameters efficiently. LogAME = 03.3 (a) LogAME = 009. (b) LogAME = (c) LogAME = (d) 00 logame loga M E Threshold LogAME = (e) LogAME = (f) LogAME = (g) (h) Figure.: Bed image enhanced using the variants of histogram equalization; (a)original bed image, bed image enhanced using (b)brightness preserving bi-histogram equalization, (c)dualistic sub-image histogram equalization, (d)minimum mean brightness error bi-histogram equalization, (e)recursive mean-separate histogram equalization, (f)brightness preserving histogram equalization ith maximum entropy, (g)bi-histogram equalization ith parameters chosen from the Logarithmic AME graph in (h) Figure. shos the output images for many different variations of histogram equalization. For images..b, c, and d, the algorithm is simple bi-histogram equalization ith an automated formula to select the threshold value. The images in..e,f are more advanced variations, and the image in..g as processed ith bi-histogram equalization using the Logarithmic AME performance measure to select the threshold. To select the threshold, the most obvious local minimum, at threshold = 35, as used. As can be seen in figure., the most visually pleasing image has been formatted ith the threshold chosen by the performance measure. This performance as confirmed by the numbers returned by the Logarithmic AME measurement, here the results are considered relative to each other. n this case, loer numbers correspond to better enhanced images. According to the quantitative measurement, bi-histogram equalization using the performance measure outperformed all of the other methods ith the exception of the histogram shaping based brightness preserving histogram equalization ith maximum entropy, a far more complex algorithm. 3. HUMAN VSUAL SYSTEM BASED MULT-HSTOGRAM EQUALZATON

6 n this section, e present the human visual system based image enhancement (HVS) model and HVS based multihistogram equalization. 3.. Human Visual System based mage Enhancement The goal of image enhancement techniques is to improve a characteristic or quality of an image, such that the resulting image is better than the original, hen compared against a specific criteria (PLP 8). Current research in image enhancement includes a number of algorithms based in some manner upon the Human Visual System (HVS), ith algorithms for color correction [5], enhancement of x-ray images [6], and gray level image enhancement [7]. Several models of the human visual system have been introduced. One method is to attempt to model the transfer functions of the parts of the human visual system, such as the optical nerve, corte and so forth. This method then attempts to implement filters hich recreate these processes to model human vision [][3]. Another method uses a single channel to model the entire system, processing the image ith a global algorithm [3]. Human Visual System based mage Enhancement (HVS) aims to emulate the ay in hich the human visual system discriminates beteen useful and useless data []. Weber s Contrast La quantifies the minimum change required for the human visual system to perceive contrast, hoever this only holds for a properly illuminated area. The minimum change required is a function of background illumination, and can be closely approximated ith three regions. The first is the Devries-Rose region, hich approximates this threshold for under-illuminated areas. The second, and most ell knon, region is the Weber region, hich models this threshold for properly-illuminated areas. Finally, there is the saturation region, hich approximates the threshold for over-illuminated areas [4]. By using this model, e have an automated method for segmenting an image into these three regions. As these regions individually have uniform illumination, traditional histogram equalization can be applied to effectively correct these issues and enhance the image. This process is further improved by calculating the threshold for each region and discarding those pixels hich do not constitute a noticeable contrast. n this manner, meaningless pixels are removed from the enhancement process, reducing artifacts. HVS image enhancement performs this segmentation using the background intensity and the rate of change information. The background intensity is calculated as a eighted local mean, and the rate of change is calculated as some sort of gradient measurement. The background intensity is arrived at using the folloing formula: B( 4 Q X ( i, j) 4 Q' X ( k, l) X ( (0) here B ( is the background intensity at each pixel, X ( is the input image, Q is all of the pixels hich are directly up, don, left, and right from the pixel, and Q ' is all of the pixels diagonally one pixel aay. We must also define a parameter B T, hich is the maximum difference in the image, arrived at using: B T X ( minx ( max () Further, the gradient information is needed, hich is arrived at in the folloing formula: G G X ( X ( y ) X ( X ( x, X '( G G ()

7 here X '( is the gradient information and G,G are the directional gradients. Finally, e must also kno some parameters concerning the human eye itself, hich e ill call B xi, i,, 3 and, i,, 3. These are arrived at using the folloing formulas: K i K B B B x x x3 3 B T B B 3 X '( max B( K K B 00 K K T T / B x x3 (3) (4) here,, 3 are parameters based upon the three different regions of response characteristics displayed by the human eye. As is the loer saturation level, it is effective to set this to 0. For, 3, it is necessary to determine these experimentally. As stated in [], it has been found that the best results occurred hen these ere set to 0. and 0.9, respectively. t is also possible to use the measures of image enhancement to select optimal values. Using this information, the image is first broken up into the different regions of human visual response. These different regions are characterized by the formula for the minimum difference beteen to pixel intensities for the human visual system to register a difference. Next, these three regions are thresholded, removing the pixels hich do not constitute a noticeable change for a human observer and placing these in a fourth image. These four images are arrived at using the folloing formula: m X ( m X ( m3 X ( m 4 X ( such that B B x x3 X '( B( Bx & K B( X '( B( Bx & K B( X '( B( Bx3 & K 3 B( All Remaining Pixels (5) mage is all the pixels in the first region for hich there is significant change in the pixel, mage is all the pixels in the second region for hich there is significant change in the pixel, mage3 is all the pixels in the third region for hich there is significant change in the pixel, and mage4 is all the remaining pixels. mage4 consists of the least important image information. 3.. HVS based Multi-Histogram Equalization Despite the improved performance of bi-histogram equalization over standard histogram equalization, extending to trihistogram equalization using the same methods does not yield better results. Comparing the results of tri-histogram equalization to bi-histogram equalization on many images shoed no consistent improvement from bi-histogram equalization to tri-histogram equalization. Another method is necessary to achieve better results using multi-histogram equalization.

8 By separating the image into regions by the quality of illumination, such as over-illuminated, ell illuminated, and under-illuminated, traditional histogram equalization can be used on each region to correct for non-uniform illumination. n order to perform this segmentation of the image, e utilize the HVS system to segment the image. The first three images are then equalized separately and unionized, and the pixels in the fourth image are then filled in according to the surrounding pixels. n summary, the algorithm is executed as follos: nput: mage to be Enhanced Step : Segment image using HVS algorithm Step : Calculate measure of image enhancement to select best values of, 3 Step : Equalize images,, and 3 separately Step 3: Recombine the pixels in the three equalized images Step 4: Fill in the missing pixels according to the surrounding pixels Output: Enhanced mage As has been shon previously, standard histogram equalization is effective for correcting uniform illumination problems. Using the proposed method, an image ith non-uniform illumination is broken up into regions ith uniform illumination, and then these regions are processed separately. This capitalizes on the strength of histogram equalization to build an algorithm hich consistently outperforms the other histogram equalization methods. This method is further improved by disregarding uninformative image pixels, alloing it to focus on only the informative image pixels and fill in the uninformative pixels at the end of the process, using the information gained from the meaningful pixels. 4. COMPUTER SMULATONS n this section, e present the results of computer simulations. We have tested the proposed method using a variety of images. These images include images ith uniform illumination, both ell and poorly illuminated, non-uniform illumination, and images ith shados. Also, e have tested ith images that ere produced by high-quality professional digitization methods as ell as loer quality cell phone cameras. We compare against the results of other histogram equalization methods. The comparison is made both by visual inspection and using the performance measures defined previously. The results sho the proposed method to produce more visually pleasing enhanced images and more consistently produce optimal enhanced images. Figure 4. shos sample results for the proposed algorithm, compared against the resulting images from the other histogram equalization algorithms. As can be seen for the duckies image, multi-histogram equalization outperforms the other algorithms on the basis of the measure. The image processed ith the proposed algorithm has much higher Logarithmic AME than the other methods. Also, by visual inspection, the images processed using bi-histogram equalization ith the performance measure and HVS based multi-histogram equalization are more visually pleasing. The objects in the background can be clearly seen and the enhanced images have better contrast. The main difference beteen the images in 4..g and 4..h is that the multi-histogram equalization method has slightly better contrast and crisper objects in the background. Figure 4. shos similar results to 4., using the all image. This is a camera phone image, taken ith a 0.3 megapixel Motorola RAZR V3 camera phone. Visual inspection shos that the all image in 4..g, here the measure is used to select the threshold, has better contrast and shos the background better than the other images, ith the exception of the image in 4..h. The image processed using multi-histogram equalization has the best contrast and also has the best definition in the over-illuminated section. Quantitatively, the measure shos that the image processed ith multihistogram equalization is the best image. t can be seen that for this image, loer Logarithmic AME numbers are improved, and the image using the proposed algorithm has the loest LogAME. Figure 4.3 shos the results for the shados image. This is a professionally captured image shoing all three regions of illumination. The desk in the foreground is under-illuminated, the building in the background is correctly illuminated, and the parking lot in beteen is over-illuminated. Many of the histogram equalization variants are able to correct one region alone, but not all three. Most of the algorithms correct the school in the background but the foreground remains too dark. MMBEBHE, shon in figure.3.c, is able to correct the items on the desk hoever much of the detail in the background is over-enhanced. Bi-histogram equalization using the measure, shon in figure.3.g, produces a more visually pleasing image, but the results can be improved upon. Clearly the best result is from the HVS

9 based multi-histogram equalization method, correcting all three regions and outscoring the other methods numerically. Again, this is a case here loer numbers from the LogAME measure are better. LogAME = (a) LogAME = (b) LogAME = (c) LogAME = 5.9 (d) LogAME = (e) LogAME = 59.5 (f) LogAME = (g) LogAME = (h) Figure 4.: Duckies image enhanced using the variants of histogram equalization; (a)original bed image, bed image enhanced using (b)brightness preserving bi-histogram equalization, (c)dualistic sub-image histogram equalization, (d)minimum mean brightness error bi-histogram equalization, (e)recursive mean-separate histogram equalization, (f)brightness preserving histogram equalization ith maximum entropy, (g)bi-histogram equalization using LogAME, (h)hvs based multi-histogram equalization LogAME = (a) LogAME = (b) LogAME = (c) LogAME = (d) LogAME = (e) LogAME = (f) LogAME = (g) LogAME = (h) Figure 4.: Wall image enhanced using the variants of histogram equalization; (a)original bed image, bed image enhanced using (b)brightness preserving bi-histogram equalization, (c)dualistic sub-image histogram equalization, (d)minimum mean brightness error bi-histogram equalization, (e)recursive mean-separate histogram equalization, (f)brightness preserving histogram equalization ith maximum entropy, (g)bi-histogram equalization using LogAME, (h)hvs based multi-histogram equalization

10 LogAME = (a) LogAME = (b) LogAME = (c) LogAME = (d) LogAME = (e) LogAME = (f) LogAME = (g) LogAME = (h) Figure 4.3: Shados image enhanced using the variants of histogram equalization; (a)original bed image, bed image enhanced using (b)brightness preserving bi-histogram equalization, (c)dualistic sub-image histogram equalization, (d)minimum mean brightness error bi-histogram equalization, (e)recursive mean-separate histogram equalization, (f)brightness preserving histogram equalization ith maximum entropy, (g)bi-histogram equalization using LogAME, (h)hvs based multi-histogram equalization 5. CONCLUSON n this paper e introduced the Human Visual System based Multi-Histogram Equalization algorithm. The performance of this algorithm and other histogram equalization based algorithms as compared using a variety of images ith uniform and non-uniform illumination as ell as standard test images and cell phone camera images. t as demonstrated that the proposed algorithm outperforms other histogram equalization variants, both numerically and visually on a range of poorly and non-uniformly illuminated images. t as further shon that the algorithm can better adapt to local image information, correcting for non-uniform illumination and shadoing effects, ithout removing all visual evidence of these effects so as not to change the underlying image information. Further, the use of the Logarithmic AME to select parameters as demonstrated, as it as shon that the Logarithmic AME is able to better select parameters for the bi-histogram equalization algorithm on the basis of visual inspection. REFERENCES [] Y. Wang, Q. Chen, B. Zhang, mage Enhancement Based on Equal Area Dualistic Sub-mage Histogram Equalization Method, EEE Transactions on Consumer Electronics, vol. 45, no., pp , Nov [] S.D. Chen, A.R. Ramli, Contrast Enhancement using Recursive Mean-Separate Histogram Equalization for Scalable Brightness Preservation, EEE Transactions on Consumer Electronics, vol. 49, no. 4, pp , Nov [3] S.D. Chen, A.R. Ramli, Minimum Mean Brightness Error Bi-Histogram Equalization in Contrast Enhancement, EEE Transactions on Consumer Electronics, vol. 49, no. 4, pp , Nov [4] C. Wang, Z. Ye, Brightness Preserving Histogram Equalization ith Maximum Entropy: A Variational Perspective, EEE Transactions on Consumer Electronics, vol. 5, no. 4, pp , Nov [5] A.M. Vossepoel, B.C. Stoel, A.P. Meershoek, Adaptive Histogram Equalization Using Variable Regions, in Proc. EEE Conference on Pattern Recognition, Rome, pp , Nov [6] M. Jourlin, J.C. Pinoli, Logarithmic mage Processing; The Mathematical and Physical Frameork for the Representation and Processing of Transmitted mages, Advances in maging and Electron Physics, vol. 5, pp. 9-96, 00.

11 [7] A. Beghladi and A. L. Negrate. Contrast Enhancement Technique Based on Local Detection of Edges, Computer Visualization of Graphic mage Processes, vol. 46, 989, pp [8] W. M. Morro, R. B. Paranjape, R. M. Rangayyan, and J. E. L. Desautels. Region-Based Contrast Enhancement of Mammograms, EEE Tran. Medical maging, vol., no. 3, 99, pp [9] S. Agaian, B. Silver, and K. Panetta, Transform Coefficient Histogram Based mage Enhancement Algorithms using Contrast Entropy, accepted by EEE Tran. mage Processing, July 3, 006. [0] E. Wharton, S. Agaian, K. Panetta, Comparative Study of Logarithmic Enhancement Algorithms ith Performance Measure, in Proc. SPE Electronic maging, San Jose, CA, #6064, Jan [] E. Wharton, S. Agaian, K. Panetta, A Logarithmic Measure of mage Enhancement, in Proc. SPE Defense and Security Symposium, Orlando, FL, #6500P, Apr [] Deng, Cahill, and Tobin, The Study of Logarithmic mage Processing Model and ts Applications to mage Enhancement, EEE Transactions on mage Processing, Volume 4, Number 4, pp , April 995. [3] M. Jourlin and J. C. Pinoli. A Model for Logarithmic mage Processing, Journal of Microscopy, vol. 49, 989, pp. 35. [4] E. Zaharescu, Medical mage Enhancement using Logarithmic image Processing, in Proc. 005 ASTED nt. Conf. Visualization, maging, and mage Processing, Benidorm, Spain, 005, paper # [5] K. Huang, Q. Wang, Z. Wu. Color mage Enhancement and Evaluation Algorithm Based on Human Visual System, EEE nternational Conference on Acoustics, Speech, and Signal Processing 004, vol. 3, pp , 004. [6] M. M. Hadhoud. X-Ray mages Enhancement using Human Visual System Model Properties and Adaptive Filters, in Proc. EEE nternational Conference on Acoustics, Speech, and Signal Processing 00, vol. 3, pp , May 00. [7] B. B. Kamel, et. al. Retinal mage Enhancement Based on the Human Visual System, in Proc. SPE Medical maging 006: mage Processing, vol. 644, pp , March 006. [8] W. K. Pratt, Digital mage Processing. Ne York: Wiley, 000. [9] R. C. Gonzales and R. E. Woods, Digital mage Processing. Reading, MA: Addison-Wesley, 987. [0] A. K. Jain, Fundamentals of Digital mage Processing. Engleood Cliffs, NJ: Prentice-Hall, 99. [] Cristian Munteanu and Agostinho Rosa, Gray-Scale mage Enhancement as an Automatic Process Driven by Evolution, EEE Transactions on Systems, Man, and Cybernetics Part B: Cybernetics, vol. 34, no., April 004 pp [] R. A. Nobakht, S. A. Rajala. An mage Coding Technique Using a Human Visual System Model and mage Analysis Criteria, in Proc. EEE nternational Conference on Acoustics, Speech, and Signal Processing 987, vol., pp , April 987. [3] J. Dušek, K. Roubík. Testing of Ne Models of the Human Visual System for mage Quality Evaluation, in Proc. EEE Seventh nternational Symposium on Signal Processing and ts Applications 003, vol., pp , August 004. [4] M. K. Kundu and S. K. Pal. Thresholding for Edge Detection Using Human Psychovisual Phenomena, Pattern Recognition Letters 4, 986, pp