BioTechnology. An Indian Journal FULL PAPER. Trade Science Inc. Improvement of low illumination image enhancement algorithm based on physical mode

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1 [Type text] [Type text] [Type text] ISSN : Volume 10 Issue 22 BioTehnology 2014 An Indian Journal FULL PAPER BTAIJ, 10(22), 2014 [ ] Improvement of low illumination image enhanement algorithm based on physial mode Shuang Niu *, Yuanyuan Shang, Hui Ding, Zhong Luan College of Information Engineering, Capital Normal University, Beijing, (CHINA) *NO.56, North Xisanhuan Road, Beijing, (CHINA) niushuang6125@gmail.om ABSTRACT The low illumination images always have low brightness, low dynami range and high noise, beause of these harateristis, an improved enhanement algorithm is proposed for low illumination image. Considering that low illumination image enhanement will amplify the noise, it needs to redue the noise before image enhanement. Firstly, using BM3D (blok mathing 3D) for image denoising in YCbCr spae, then improving the transmission for image enhanement based on the physial model in HSI spae. Experiments show that the method an effetively improve the brightness and ontrast, enhane the detail of the image, redue noise, and get a good visual effet. KEYWORDS Low illumination; Image enhanement; Physial model; Blok mathing 3D. Trade Siene In.

2 13996 Improvement of low illumination image enhanement algorithm based on physial mode BTAIJ, 10(22) 2014 INTRODUCTION Low light onditions have no assistant light soure, images obtained on rain day, at night or in the mine usually have poor quality and blurred details, these images are not applied to mahine reognition and target traking, so the resulted is unusable for pratial appliations. As image aquiring systems are demanded to work under low light onditions, the image enhanement and noise redution is highly desired. Conventional image proessing tehniques suh as histogram equalization whih is the most widely used, it enhanes ontrast through simple omputing, but leads to struture information loss [1]. The Retinex an maintain olor onstany of human vision. However, it auses some problems, suh as halo effet, gray-out result and noise amplifiation [2][3]. In addition, Zhao et in [4] proposed a spatial domain enhanement method ombined gradient transform with high boost filter, Yin et in [5] adopted bilateral filter and luminane statistis in order to ompensate brightness, A nonlinear ontrast enhanement based on human visual system was presented in [6], Spae-variant luminane map has been used in [7] to enhane the low illumination image, A homomorphi filtering based on HSV spae in [8] solves the problem of olor ast, these methods do not onsider the harateristis of low illumination image, the results are not ideal. In reent time, Dong et in [9] propose ontrast enhaning method based on dark hannel prior, and then Zhang et in [10] apply image de-hazing algorithm used luminane omponent to the inverted low illumination image, the algorithm is fast and effetive, however, when the bright spot exists or the sene depth is disontinuous, the boxes will appear whih is disappointing. To solve the above problems, the paper proposes an improved algorithm building upon work in [10]. Aiming at the serious noise of low illumination image, we put the image de-noising before enhanement, whih ould avoid inreasing the noise when enhane the image. Besides, improving the estimate of the transmission overomes the details lost phenomena beause of the rough luminane transmission. LOW LIGHT IMAGE DENOISING BASED ON BM3D Low illumination images have very high noise, and there is a high orrelation between the image and the noise, diretly enhaning the image will inrease noise aordingly, so we need to redue the noise before. Analysis of low illumination image [11], the noise mainly inludes impulse noise generated during transmission and storage, Gaussian noise produed by various omponents and transmission hannel, and poisson noise under the ondition of very low illumination, where poisson noise impats on the low illumination image biggest, therefore, 3D de-noising is more suitable, Dabov K, Foi A et [12] proposed a three-dimensional blok mathing de-noising (BM3D) is onsidered as the best de-noising method at present, this method is a kind of enhaned sparse representation based on transform domain. The algorithm steps is illustrated in Figure (1)., and the main ideas and steps are as follows, Firstly, staking similar 2D image neighborhoods into a 3D array that alled grouping, then getting the optimal estimation using ollaborative filtering, the obtained blok estimates may overlap after the above proessing, and thus we need to aggregate these multiple estimates by weighted averaging. In order to obtain a better result, above general proedure is used repeatedly. Image de-noising in HSV olor spae need to deal with eah hannel, however, olor is sensitive to the hange of H and S omponent, espeially H omponent, therefore, de-noising in HSV olor spae will lead to olor distortion. In YCbCr spae, beause the human eyes are relatively sensitive to Y omponent but not sensitive to Cb and Cr omponent, so the denoising does not ause olor inonsisteny. Considering de-noising algorithm operation effiieny and olor fidelity simultaneously, we use the YCbCr spae as the de-noising experimental sene, in YCbCr spae, analysis of noise level of eah hannel, the luminane omponent Y ontains higher level noise, therefore, this artile only deals with the noise on the Y omponent, that improves the proessing effiieny. Noisy image Step 1 Basi estimate Step 2 Final wiener estimate Blok-wise estimate Aggregation Blok-wise estimate Aggregation Inverse 3D transform Grouping by blok-mathing n Inverse 3D transform Grouping by blok-mathing Hard-threshold Weight Wiener filtering Weight 3D transform 3D transform Figure 1 : Flowhart of BM3D de-noising algorithm

3 BTAIJ, 10(22) 2014 Shuang Niu et al RELATED WORK This algorithm is based on the physial model, parameter estimation is the key in the physial model, Koshmieder physial model and dark hannel prior is used to estimate parameter. Physial model The lassial physial model an be expressed as follows: I( J ( A(1 e d ( (1) (2) Where A is the atmospheri light, x is the spatial loation, I( is the intensity of the observed image that is the fog image, J( is the lear image after dehazing, is the transmission funtion, is the atmospheri sattering oeffiient, and d( is the sene depth. In (1), the first term is the diret attenuation aused by the atmospheri sattering during transmission from the sene to imaging devies, and the seond term is the airlight aused by natural light sattering. Dark hannel prior He et [13] get the dark hannel prior by observation and statistis of 5000 images, and this theory reates a new field on image dehazing. To any image J, we define the dark hannel as follows: J dark ( min( min y ( { r, g, b} J ( y)) (3) Where J is a olor hannel of J, ( is a square area entered at x. Aording to dark hannel prior, if J is an outdoor haze-free image, J dark is always very low and tends to be zero. THE PROPOSED ENHANCEMENT OF ILLUMINATION IMAGE In order to enhane the image that is proessed with de-noising algorithm, the enhanement in this artile is proposed on the base of physial model. Aording to the observation, the pixel of every olor hannel in sky and far bakground part will be high after low illumination image reversed. Meanwhile, there has at least one hannel luminane is low in the non-sky area whih is similar with the image that get under the ondition of thik fog. Therefore, it ould be work that apply de-hazing algorithm to low illumination image that has been reversed for enhaning it. It means that the enhaned low illumination image ould be got after reversing the reversed low illumination image that is proessed by de-hazing algorithm. During the proessing of de-hazing in this paper, Luminane transmission map instead of atmospheri transmission map in physial model is estimated by omponent I in HIS olor spae, whih will improve the luminane of the enhaned image and subtilize the details after gaining the thinning from Retinex. Enhanement of low illumination image based on physial model Considering the enhanement of low illumination image, it is the first step to reverse the initial image R, and the formula is stated as follows: I ( 255 R ( (4) stands for RGB olor hannel, I ( is the reverse of low illumination image, R ( is initial low illumination image. After that, we an dehaze I with formula (1) and make use of dark hannel prior to estimate atmospheri light A with He [8] method. First and foremost, we hoose 0.1% pixel that is the maximum luminane in dark hannel, then the pixel orresponding to the maximum pixel in initial image is atmospheri optial. The formula for Restoring image is shown in (5): J ( I( A A (5) The image that has been defogged will be applied to formula (4), then it ould get enhaned low illumination image E, as illustrated in Figure (2).(e).

4 13998 Improvement of low illumination image enhanement algorithm based on physial mode BTAIJ, 10(22) 2014 (a) (b) () (d) (e) Figure 2 : (a) Input low light image, (b) Inverted result from input image, () Estimated transmission map, (d) Hazefree image, (e) Final output It ould be seen from formula (5) that the point of restoring image is estimated luminane transmission map reasonably, so do this paper. The estimation of luminane transmission map will be introdued onretely in next hapter. Luminane transmission map estimation of low illumination image Apply dark hannel prior to fog physial model, in the ase of the fat that A is known, assume that transmission rate is onstant in loal area, minimize two sides of formula (1), as shown in (6): I ( y) min( min ( )) y ( A J ( y) min( min ( )) (1 ) y ( A (6) Under the ondition of haze-free, dark hannel value is lose to zero, we an get as follows: I ( y) 1 min ( min ( )) { rgb} y ( A (7) It ould be seen from (2) that is dereased with the inrease of sene depth, the farer senery has the smaller transmission rate, as the same as the thik fog area. However, we find that the reversed image of low illumination image is not real fog image, its transmission map and brightness is related to eah other losely whih is not like the fog image that dereased with sene depth. In this ase, the transmission map ould be estimated based on brightness omponent, beause the darker plae in initial image will make the fog thiker in the reversed image aordingly, as illustrated in Figure (2).(b). If we would like to use luminane transmission map instead of initial atmospheri transmission map to restore image, transit RGB image to the image that make the hromatiity and luminane apart. The ommon olor spae is HSI, HSV, YCbCr, Lab et. Eah olor spae has advantage and disadvantage. Zhang estimate transmission map with the omponent of omponent Y in YCbCr spae, but if we enhane the lumination in YCbCr will lead to the olor fade away. HSI start from the visual system of human, whih keep the onstany of olor, so it wouldn t hange the olor if we expand the luminane and ontrast in HSI. The luminane ould be stated as follows: I ( R G B) / 3 (8) Brightness omponent image is quite different with the atmospheri transmission map. In order to make the luminane transmission map instead of atmospheri transmission map, and make both approximate on effet and funtion, we an use the suited parameter C minus the pixel values of eah point, the minus result make the inverse transformation, but the estimated transmission we get in this method is very rough, the effet of proessing the image is not that satisfied. So we have to refine the luminane transmission. MSR has multiple sales, whih is a ombination of maximum, medium and minimum sale advantage an realize ompression and inrease the edge details in the dynami range at the same time. This paper will use MSR proess luminane omponent, as shown in formula (9): I m ( x, y) W n 1 (ln I ( x, y) ln[ F ( x, y) * I ( x, y)]) n N n (9)

5 BTAIJ, 10(22) 2014 Shuang Niu et al After MSR proessing, the luminane omponent will get reverse olor transformation, then get the oarse estimates of the transmission: ~ t ( C I m Median filtering the outline of luminane transmission in the estimated transmission, for making defogged image ontain more detail information [14], thus got the final luminane transmission map, as shown in Figure (2). (). RESULTS Aording to the above algorithm, low illumination image enhanement, Figure (3). shows the enhaned results and the estimated luminane transmission map in two different senes. From Figure (3)., we an see that the proposed algorithm eliminates the noise effetively and improves brightness and edge details exellently. Meanwhile, luminane transmission map details also have ertain advantages. (10) (a) (b) () (d) (e) (f) Figure 3 : (a) and (d) Input low light image, (b) and (e) Estimated transmission map, () and (f) Final output Figure 4 : Left: low light examples. Middle: results by Zhang s algorithm. Right: results by the proposed algorithm

6 14000 Improvement of low illumination image enhanement algorithm based on physial mode BTAIJ, 10(22) 2014 To further verify the effetiveness and the superiority of the proposed algorithm, Figure (4). shows the omparison of Zhang algorithm and the proposed algorithm in four different senes. From Figure (4), olor distortion is serious by Zhang algorithm, and the result by the proposed algorithm is better in olor fidelity. In terms of de-noising, the proposed algorithm is obviously better than that of Zhang. In addition, the proessed image brightness is also slightly higher than that of Zhang. In addition to the subjetive visual effet, it also need the objetive parameters to evaluate the superiority and inferiority of the proposed algorithm. To the enhaned image, it will evaluate from two aspets -image fidelity and denoising. Therefore, we introdued the AMBE (absolute mean brightness error) and PSNR (peak signal to noise ratio) as objetive evaluation parameters of enhanement effet. AMBE measures the image fidelity, the smaller AMBE value is, the higher fidelity will be. PSNR measures de-noising effet, the greater the PSNR is, the better de-noising effet will be. TABLE 1 lists the ontrast result of objetive parameters of four images in Figure (4).. TABLE 1 : The ontrast result of quality evaluation parameters of four example images Input image example Quality evaluation Zhang s algorithm The proposed algorithm Example 1 of Figure (4) Example 2 of Figure (4) Example 3 of Figure (4) Example 4 of Figure (4) AMBE PSNR AMBE PSNR AMBE PSNR AMBE PSNR Seen from the results, in ontrast of the image from three groups, AMBE we get from the algorithm in this paper are less than the algorithm of Zhang, PSNR are more than Zhang algorithm, so it learly shows that this algorithm also has ertain advantage of fidelity and de-noising. CONCLUSIONS Aording to the harateristis of low illumination image, we propose a low illumination enhanement algorithm based on physial model. Putting image de-noising before the enhanement of low illumination image an avoid the problems that noise amplifiation aused by the image enhanement. The images obtained at night ontain high noise, and most of the noise is the poisson noise, so 3D de-noising algorithm suits the image de-noising. Using BM3D algorithm to deal with Y omponent only ould ensure de-noising and avoid the olor distortion at the same time, and improve the effiieny. When we do de-hazing for the inversed low illumination image, Retinex theory and brightness figure are ombined to estimate luminane transmission map, whih will further improve brightness and ontrast of the image, strengthen the details as well. This artile ontent has no onflit of interest CONFLICT OF INTEREST ACKNOWLEDGEMENT This work was supported by National Natural Siene Foundation of China (No , , and ), Beijing Natural Siene Foundation of China ( ), and Sientifi Researh Base Development Program of the Beijing Muniipal Commission of Eduation. REFERENCES [1] P.Rajavel; Image dependent brightness preserving histogram equalization, IEEE Trans, Consumer Eletronis, May, 56(2), (2010). [2] B.Li, S.H.Wang, Y.B.Geng; Image enhanement based on retinex and lightness deomposition[c], th IEEE International Conferene on Image Proessing.Beijing, China, (2011). [3] C.An, M.Yu; Fast olor image enhanement based on fuzzy multiple-sale retinex [C], 2011 The 6th International Forum on Strategi Tehnology, Chongqing, China, (2011). [4] W.J.Zhao, Z.Cao, P.Liu; A ombining spatial enhanement method for low illumination images [C], 2013 Fourth International Conferene on Emerging Intelligent Data and Web Tehnologies, EIDWT, 135( ) (2013).

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