LATEST TRENDS on COMPUTERS (Volume II)

Transcription

1 ENHANCEMENT OF FOG DEGRADED IMAGES ON THE BASIS OF HISTROGRAM CLASSIFICATION RAGHVENDRA YADAV, MANOJ ALWANI Under Graduate The LNM Institute of Information Technology Rupa ki Nangal, Post-Sumel, Via-Jamdoli Jaipur ,Rajasthan INDIA Abstrac: - Bad weather, such as fog and haze, can significantly degrade the visibility of a scene. Optically, this is due to the substantial presence of particles in the atmosphere that absorb and scatter light due to this captured images degrade significantly. This leads to accidents in air, on sea and on the road. This paper presents image enhancement methods for fog degraded images depending on the histogram based classification of the images.this methods does not require any environmental condition to be known in which image is captured or multiple images,it requires only single input image Here we first classify the given image into one of the four classifications (left, middle,right and right with peak) and based upon its class, we propose methods for the enhancement of its visibility. Key-Words: - Scene depth,histogram classification 1. INTRODUCTION Images taken under bad weather conditions suffer from degradation and severe contrast loss. The degree of degradation increases exponentially with the distance of scene points from the sensor. The standard filtering methods cannot restore images degraded by bad weather conditions like fog, mist, haze etc, hence contrast enhancement methods are used. There can be two approaches for improving the visibility of fog- degraded images. One is based on the atmospheric model and the other is based on the contrast enhancement. 1.1 Atmospheric Model These methods use physical models to predict the pattern of image degradation and then restore image contrast with appropriate compensations. They provide better image rendition but usually require extra information about the imaging system or the imaging environment. Oakley et.al [1,2] used a physics-based method to restore scene contrast without any predicted weather information by approximating the distribution of radiances in the scene by a single Gaussian with known variance, however, in most of atmospheric based models, scene depth need to be estimated beforehand, but it requires more information about the scene environment, like multiple degraded images taken from the same point or both the clear day and foggy day images. Narasimhan and Nayar use two or more different bad weather images taken from the same point of view to restore scene structure and contrast based on atmospheric scattering model by assuming the atmospheric scattering properties invariable [3,4]. Narasimhan et. al. [5], presented an interactive scene depth estimate method, in which the image contrast can be restored using a single image when the biggest and smallest scene depth is assigned beforehand. All methods based on physical model either need scene depth information to be known beforehand or multiple degraded images taken from the same point, these requirements make this approach impractical in some cases. 1.2 Contrast Enhancement The most commonly used contrast enhancement method is histogram equalization and its variations. Some other image enhancement techniques are also presented by many researchers [6-9], in order to restore contrast of fog-degraded images, which do not need any information regarding the scene depth and avoids complicated atmospheric scattering model. All the techniques proposed, assumes a certain set of attributes of the fog degraded images, which limits them to be considered for all types of fog degraded images, for instance histogram equalization may not improve contrast of the image which lies in right range of histrogram. As an illustration, Figure-2 below shows the result of histogram equlization when ISSN: ISBN:

2 applied on Figure-1 which shows that visibility after histrogram equilization may not improve or there can be added noise which degrades the visibilty. Thus, addressing to the classification based methods becomes important and necessary. In this paper, we present image enhancement methods, after classifing the image based on its histrogram, to enhance its visibility Middle range histrogram images Images having concentration of pixels in the range of 50 to 200, belongs to this class. 50 Histogram [ I ( x, ] 200 (2) 2.3. Right histrogram images The images which lie in this class have their histrogram to the right of histrogram curve i.e concentration of pixels is more in the range of 100 to Histogram [ I ( x, ] 255 (3) Figure 1 Figure 2 Figure 3 2. Classification of images A image histrogram gives the information about the number of pixels with the intensity values from 0 to 255 (for gray images). According to histrogram curve we have classified the images in the following: 2.1. Left histrogram images Images whose pixel values are concentrated in the range of 0 to 150 lies in this class. 0 Histogram [ I ( x, ] 150 (1) 2.4. Right histrogram peaks on right side In this class of images there is a peak on right side of the histogram. At this gray level normally there are sky pixels but because of higher concentration of pixel at these values there may possibly be a object with same intensity value as that of the sky region, so this class of image is also defined. In these images more concentration of pixels in histrogram images is in the range of 200 to Histrogram [ I ( x, ] 255 ( 4) 3. Image Enhancement Methods: In this section we present methods for enhancing the above mentioned classes of the image. Here in order to improve the visibility of fog degraded images we use the contrast enhancement techniques and tranform based filtering. Histogram equalization is one of the popular contrast enhancement algorithm for its simplicity and effectiveness, but since it uses global histogram information over the whole image as its transformation function in order to stretch contrast, it may not reflect local scene depth change, the enhancement effect may not be satisfying when depth changes in the scene. An example shown in section 1 illustrates this fact. In order to minimize these effects we can use block based histrogram equilization in which a mask moves through the image and applies histrogram equilization to every block after evaluating some conditions on block which are described in next section. For the images which belongs to right histrogram classe, visibilty is enhanced by linear stretching. Images with low illumination are enhanced using transform based filtering. For Middle histrogram class images, we first ISSN: ISBN:

3 find edge pixels in the image, if there is an edge those pixels are left unaltered and contrast enhancement is applied on nonedge pixels. Detailed description of these methods is presented in the next section: 3.1. Enhancement of Left Histrogram Image In this class of images concentration of pixels is towards left of the histrogram i.e. there is a bad illumination condition.here we use transform based filtering. We are dealing with grayscale images, we can say that when an image is generated from a physical process, its values are proportional to energy radiated by a physical source. In other words, an image is an array of measured light intensities and is a function of the amount of light reflected from the objects in the scene.the intensity is a product of illumination (the amount of source illumination incident on the scene being viewed) and reflectance (the amount of illumination reflected by the objects in the scene). If we denote illumination as L( x, y ) and reflectance as R( x, y ), then an image I( x, can be expressed as: I( x, L( x,. R( x, (5) The model of described above known as the illumination-reflectance model and can be used to address the problem of improving the quality of an image that has been acquired under poor illumination conditions.illumination results from the lighting conditions present when the image is captured, and can change when lighting conditions change. Reflectance results from the way the objects in the image reflect light, and is determined by the intrinsic properties of the object itself, which (we can safely assume in this theoretical analysis) does not change.illumination varies slowly in space (slow spatial changes low spatial frequencwhile reflectance can change abruptly (high spatial frequencies).so in the bad illumination condition of foggy images we will like to enhance reflectance while reducing the contribution of illumination.hence, we need to somehow separate the two components from the image and then high pass the resulting image in frequency domain. Transform based filtering is a frequency domain filtering process as described below:- M(( x, ) log( I( x, ) log( R( x, ) log( L( x, ) (6) T ( M) T (log R) T(log Fm( u, v ) Fr ( u, v) Fl( u, v) (7) HereT = Transform in frequency domain Fr and Fl are transform of reflectence and illumination components.now we can high pass the Fm( u, v) from high pass filter H( u, v ) and get the reflectence components S( u, v ) mainly. S( u, v ) H( u, v). Fm( u, v) = H( u, v). Fr ( u, v) H( u, v). Fl( u, v) (8) now we take the inverse transform of the S( u, v ) : K( x, IT( S( u, v)) (9) Now the enhanced (filtered) image I '( x, can be obtained by exponential operation. I '( x, y ) exp( K( x, ) (10) So we can briefly define Transform based filtering steps as follows:- I( x, log T H( uv, ) IT exp I'( x, We can use any kind of transform for this method. In our experiment we used fourier transform for this purpose and gaussian filter for filtering. In transform based method because of property of taking out reflectance pixel from image this method work in low illumination or left histrogram images. 3.2.Enhancement Of Middle Histrogram Image Images which lie in this class suffer from bad visibility in different parts of the whole image. Some area of image have poor contrast due to fog, whereas some area may not have very poor contrast,since it may not be affected by fog.hence these class of images have to be enhanced depending on whether a part is affected by dense fog or not.here we use,hvs characterstics in order to identify two areas.according to HVS characterstics,if a pixel is an nonedge or edge pixel it will define that it whether it is affected by fog or not respectively.hence complete image is searched for edge and nonedge pixels and local enhancement is made only on the non edge pixel as they are assumed to affected by dense fog, whereas edge pixels are left unaltred. For enhancing nonedge pixels we use local histrogram equilization which improves the contrast of the selected part of the image. A brief description of the algorithm is as follows:- Step1: Input M x N original fog-degraded image as I ( x, and take a block B( x, of size m x n. Step 2: Find the pixel of highest gray level in the Image. H max( I ( x, ), x [1 to M ], y [ 1 to N ] ; Step 3: Get the bright region of the image.br H TH, Where TH Threshold Which is kept 25 for our experiment. ISSN: ISBN:

4 Step 4: Take a output image O ( x,,and two matrix of size M x N AVG( x, and COUNT ( x, initially having all zero in these and variable A 1, B 1, stepsize 1, K, L. Step 5. Put the block B in input image i.e. B (1,1 ) at I( A, B ) Take the corresponding pixel values of input image in block.now if B( x, BR, B ( x, 255 ; Now find out gradient at every pixel in B, horizontal gradient H B( i, B( i 1,, vertical gradient V B( i, B( i, j 1), if ( H V Th1) B( i, edge pixel ; else B( i, nonedge pixel ; Th 1 is threshold to differentiate edge and non edge pixel.get the nonedge pixel and apply histrogram equilization on them and left edge pixel unaltered. The bright region pixel are given value of 255,since prior to contrast enhancement,as there may be possibility that the nonedge pixels after applying contrast enhancement method falls into this bright region. Step6: AVG( K, AVG( K, B ; COUNT ( K, COUNT ( K, 1; K [1 to A m 1] ; L [1 to B n 1] ; step 7: move the block now in raster order (Left to Right and up to down) to cover whole image. if( A M ) A A stepsize ; move at Step 5 ; Else if(b N ) B B stepsize ; A 1; move at Step 5 ; Else BREAK Higher stepsize than 1 reduce the time complexity of the algorithm, while there will be no significant loss enhancing the visibility. In our experiment for Blocksize 64 x 64 we take stepsize 10. Step8: O( x, AVG( x, / COUNT ( x, ; 3.3 Enhancement of Right Histrogram Images: In this class of images there are more white pixels.hence we use linear stretching for contrast enhancement of these images.histrogram equilization produce noise in this class of images,since the pixels are concentrated toward bright region and after histrogram equilization these pixels are mapped towards dark region,which degrade the visibility as shown in Figure-3 so we use linear stretching based method.we used the algorithm which we used in middle range histrogram images just we change the step 5 like this: Step5: Put the block B in input image at I( A, B ).Take the corresponding pixel values of input image in block.now If B( x, BR, x m, y n B( x, 255 ; For other nonbright pixel apply linear stretching. We don t apply the histrogram equilization in this class of images because even after predicting bright region there are many pixels which are bright and when we apply histrogram equilization on them these pixels move toward bright region and produce salt and peeper kind of noise while linear stretching produce less noise. 3.4.Enhancement of Right Histrogram peak Images In these class of images since we have a peak at right hand side of histrogram so there may be some objects with bright intensity range in image.now if there are some objects in the image then linear stretching will not work because when we predict the bright region the object will also be included in that range and since we move all the bright pixels to 255 hence object information is lost. In this condition transform based filtering is used is used where property of seperating illumination and reflectance is used to seperate the object and bright region of the image which further can be filtered. Here linear stretching based method can also be applied,which gives better visibility of fog effect area in the image,but introduces some distortion also as shown in Figure Result and conclusion: ISSN: ISBN:

5 LATEST TRENDS on COMPUTERS (Volume II) Figure 7: Middle histrogram with peak Figure 4: Left Histogram image Figure 8: Histrogram of Figure 7 Figure 5: Histogram of figure 4. Figure 9:Edge enhancement of Figure 7 Figure 6: Transform filtering of Figure4 Figure 10:Right Histrogram image Figure 7: Middle histrogram with peak Figure 11:Histrogram of Figure 10 ISSN: ISBN:

6 LATEST TRENDS on COMPUTERS (Volume II) Figure 12: Linear streching of Figure 11 Figure 16: linear stretching of figure 1 which shows more visible but distortion is here 5. References [1] John P. Oakley, Brenda L. Satherley, Improving image quality in poor visibility conditions using a physical model for contrast degradation, IEEE Transactions on Image Processing, [2] Kok Keong Tan, P. Oakley, Enhancement of Color Images in Poor Visibility Conditions, Image Processing, Proceedings, 2000 International Conference on, 2: [3] S. G. Narasimhan, S. K. Nayar. Chromatic Framework for Vision in Bad Weather. Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Vol.1, 2000: [4] Y. Y. Schechner, S. G.Narasimhan,S. K. Nayar. Polarization-Based Vision through Haze. Applied Optics, Special issue "Light and Color in the Open Air".Vol.42 (No.3) 2003: [5] S. G. Narasimhan, S. K. Nayar. Contrast Restoration of Weather Degraded Images. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI).Vol.25 (No.6),2003: [6] Zhu Pei, an image clearness method for fog, xi an University of Technology, [7] Luo Yingxin, poor contrast foggy image enhancement algorithm research, Tianjin University, [8] Li Peng, method of removing fog effect from images,nanjing University of Technology, [9] YI-SHU ZHAI, XIAO-MING LIU.An Improved fog degraded image enhancement algorithm., Dalian Maritime University, [10] Robby T. Tan, Visibility in BadWeather from a Single Image, Imperial College London,2008. [11] Dongjun Kim, Changwon Jeon, Bonghyup Kang and Hanseok Ko, Enhancement of Image Degraded by Fog Using Cost Function Based on Human Visual Model,2008. [12] Girish Singh Rajput, Zia-ur Rahman, Hazard Detection on Runways using Image processing Techniques, Figure13:Right histrogram peak Image Figure 14: Histrogram of Figure Figure 15: Transform filt ering of Figure13 ISSN: ISBN: