New adaptive histogram equalisation heuristic approach for contrast enhancement

IET Image ProcessingVolume 14, Issue 6 p. 1110-1119 Research ArticleFree Access New adaptive histogram equalisation heuristic approach for contrast enhancement Shubhi Kansal, Corresponding Author kansalshubhi@yahoo.com Electronics and Communication Department, National Institute of Technology, Narela, New Delhi, IndiaSearch for more papers by this authorRajiv Kumar Tripathi, Electronics and Communication Department, National Institute of Technology, Narela, New Delhi, IndiaSearch for more papers by this author Shubhi Kansal, Corresponding Author kansalshubhi@yahoo.com Electronics and Communication Department, National Institute of Technology, Narela, New Delhi, IndiaSearch for more papers by this authorRajiv Kumar Tripathi, Electronics and Communication Department, National Institute of Technology, Narela, New Delhi, IndiaSearch for more papers by this author First published: 05 April 2020 https://doi.org/10.1049/iet-ipr.2019.0106Citations: 1AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinked InRedditWechat Abstract Contrast enhancement of an image can be performed by using a simple histogram equalisation (HE) technique. However, there are some drawbacks of HE like immense brightness change, artificial effects, over-enhancement, which make it unsuitable to be used in many applications. To resolve these issues a new adaptive heuristic HE approach is proposed in this study. First, probability distribution function (PDF) of the image is calculated. Second, an adaptive parameter is calculated based on the mean and maximum values of that PDF. Thereafter, PDF and cumulative distribution function (CDF) are modified by applying a threshold limit to that adaptive parameter. Finally, another adaptive parameter is finding out by using modified CDF and a new CDF is obtained by using this second adaptive parameter. Traditional HE is then applied with the new CDF to getting the enhanced image. The visual and quantitative results of the proposed method outperform all other state-of-the-art papers and works well both for low and bright contrast images simultaneously. After rigorous experiment, it is concluded that the authors' method enhances the image contrast very well with no over-enhancement or artificial effects in the images and also preserves the original characteristics of the input images. 1 Introduction Image enhancement is an essential and frequent process in image processing [1]. Before an image can be utilised for any further application, image enhancement is required for a clear vision. It is extensively utilised for medical image processing, remote sensing imaging, character recognition, biometric recognition surveillance system and many more. Histogram equalisation (HE) is a straightforward and frequently used technique for image enhancement purposes due to its effectiveness and immediate results. It generally distributes the image grey levels evenly throughout the range, but it also changes the overall brightness of the image due to which artefacts are introduced in the image. As a result instead of enhanced look, the image looks unnatural and noisy. To overcome these issues numerous global HE (GHE) methods have been proposed after modifying the basic HE method. Brightness preserving bi HE (BBHE) was given in 1997 by Kim [2] for preserving the image mean brightness. BBHE divided the original image histogram into two portions based on its mean and then each individual histogram was equalised separately. Dualistic sub-image HE (DSIHE) [3] was proposed in 1999 similar to BBHE, but here the histograms were divided on the basis of the median instead of mean. Both of these techniques succeeded in preserving the image mean brightness, but failed to conserve the natural features. Recursive mean separate HE [4] and recursive sub-image HE [5] were proposed in 2003 and 2007, respectively. Both methods preserved image brightness much better than BBHE and DSIHE. Basically image histograms were separated recursively on the basis of mean and median, respectively. The number of separations of image histogram preserved the image mean brightness up to significant extent, but it increased the complexity of the system. In 2009, the histogram modification framework (HMF) [6] was proposed for contrast enhancement. In this method, the level of contrast enhancement was adjusted based on some optimisation parameters which reduced the cost function. This method enhanced the image, but it introduced artefacts in some images with slope histogram spikes. Again, in 2010, a histogram modification scheme was proposed [7]. Two boundary values were found and probability density function (PDF) of the image was changed according to those values. This method has limited the brightness of the image. In 2010, quadrants dynamic HE [8] was proposed for contrast enhancement. This method divided the histograms into four sub-histograms based on the median and then every four histograms were equalised independently. This method lost the natural characteristics of the images. Adaptive gamma correction with weighting distribution (AGCWD) [9] was proposed in 2013. This method applied gamma correction and the probability distribution of luminance pixels for contrast enhancement. This method worked good for plain images, but it failed on the images, which contained very less bright pixels. A quantile-based HE [10] method was proposed for brightness preservation and contrast enhancement in 2014. This method segmented the histogram on the basis of the quantile values. Though this method preserved the image brightness, it failed to bring out the details of the image. The exposure-based sub-image HE (ESIHE) [11] method was also proposed in 2014. This method enhanced the contrast of the image very well and also avoided the over enhancement problem of HE by utilising the clipping process. However, it failed to preserve the image brightness. A piecewise HE method was proposed for dark image enhancement [12]. This method focused on maintaining the image intensity as well as contrast boosting. However, since this method was specially designed for dark images, it did not work on bright and normal light images. Entropy maximisation histogram modification [13] scheme was proposed in 2015 for contrast enhancement. It comprised pixel populations mergence (PPM) step and the grey-levels distribution step for merging of non-zero bins. This method resulted in saturation artefacts in the images. In 2016 entropy-based dynamic sub-HE [14] was proposed for contrast enhancement. In this method, the histograms were divided recursively based on the entropy, and then equalisation was performed independently. Since this method also involved recursive division, it increased the complexity of the system. In 2017, another method was proposed based on gamma correction and addition histogram modification (GCAHM) [15]. In this sum of the input histogram and its standard deviation were computed. Then, gamma correction was applied to the result sum to generate a modified histogram. And finally, the traditional HE was applied for enhancement. This method enhanced the contrast of some images very well, but showed limited enhancement for some images. Recently in 2018, a plateau limit-based tri-HE (PLTHE) [16] was proposed for image enhancement. In this method, the histogram of the image was clipped based on the plateau limit, and then it was divided into three parts using histogram subdivision limit parameters that were calculated on the basis of the standard deviation of the image. Some heuristic methods have also been proposed for enhancement. A heuristic-based face recognition technique was proposed [17] in 2011. In this method, multiple faces were studied based on different criteria over a period of time, and then heuristic supplemented PCA algorithm was applied to find the optimal solution. In 2014, a heuristic algorithm for extracting useful information from an image was proposed [18]. This method applied a heuristic approach for edge detection from the images. It used very basic concepts of image processing to reach its goal. In this paper, a new and simple adaptive HE method is proposed for image contrast enhancement. It is a heuristic approach. Basically, the heuristic technique is not based on any specified model or algorithm; instead it is a practical approach adopted for finding the solution to the problem. Here, trial and error heuristic method is used to find the best results after testing on number of images. First of all, PDF of the image is calculated. Then, an adaptive parameter is calculated based on the mean and maximum value of that PDF. Thereafter, PDF and CDF are modified by applying a threshold limit to that adaptive parameter. Finally, another adaptive parameter is finding out by using modified CDF and a new CDF is obtained by using this second adaptive parameter. Traditional HE is then applied with the new CDF to getting the enhanced image. The proposed algorithm is implemented and compared to other state-of-the-art papers visually and using image quality metrics such as entropy, quality index (QI), quality-aware relative contrast measure (QRCM), structural similarity index measure (SSIM) and colour quality enhancement (CQE). The experimental results show that the proposed method provides excellent visual quality of the images and also balanced parameter results. This paper is organised as follows: Section 2 gives the motivation behind the proposed work. Section 3 describes the proposed technique. Section 4 gives experimental results and discussion, and finally, Section 5 concludes the paper. 2 Motivation The purpose of proposing this particular technique is to enhance the contrast of both low and bright images. As discussed in the above section all state-of-the-art methods have some or the other drawbacks linked with them such as Mean-shift in the brightness of the image. Over-enhancement of the images. Loss of natural characteristics of the input image. Increased complexity of the system due to a large number of calculations. Saturation artefacts in the images. Limited enhancement of the images. The proposed technique can overcome these problems and results in a visually appealing image with high contrast and natural look. 3 Proposed adaptive HE method This section describes the proposed adaptive HE method. Fig. 1 shows the flowchart of the proposed work. Let an image input having intensity from , where L is 256. Its histogram is calculated and represented as . The PDF of kth intensity level is given as (1)where denotes the size of the image. Now, we find the mean and maximum values of the above PDF as (2) (3) Fig. 1Open in figure viewerPowerPoint Flowchart of the proposed method 3.1 Calculation of adaptive parameter (R) and PDF modification Now, we define an adaptive parameter (R) as (4)It is adaptive in the sense as and values will be different for different images. Now, we apply a threshold limit to this adaptive parameter and modify the PDF accordingly. If R > = 10 than This threshold limit is chosen heuristically after testing on various images. If the value of R is <10, then PDF of the image will remain as it is without any change. 3.2 Calculation of CDF and its modification Now, the CDF of the image is calculated as (5)Again, the above condition is followed and CDF has modified accordingly: If R > = 10 (6)where is the minimum value of . Also, we normalise this CDF (7)Hence, it is clear that if R < 10, then CDF will be standard CDF of the image without any changes in it. 3.3 Calculation of second adaptive parameter () and new CDF Now, we find another adaptive parameter using above CDF as (8) is adaptive as value will be different for different images. This value is also chosen heuristically after testing on a number of images. Now, a new CDF is computed using the value of (9)Finally, HE transfer function is applied as (10)where is the final enhanced image. It is clearly seen from above that if R < 10 than standard HE procedure will be followed and then after (8) and (9) will be executed for enhancement. The above procedure is a complete heuristic approach that is adopted after testing on various images from different databases. Since this method is specifically applied on colour images, therefore, RGB image is first converted into an HSV colour space, and then the V component (matrix) is converted into an intensity image whose values range from 0 to 255. Then the proposed method is applied to this converted intensity image. In the end, the image is back converted into RGB image to get the final enhanced image. Algorithm 1 (see Fig. 2) shows the pseudocode for the proposed method. Fig. 2Open in figure viewerPowerPoint Algorithm 1: The proposed algorithm 4 Experimental results and discussion The proposed method has been implemented and compared with other state-of-the-art papers like HE, HMF, AGCWD, ESIHE, GCAHM and PLTHE. Images are taken from the different datasets, i.e. the Berkeley image database [19], Kodak lossless true colour image suite [20] and computer vision group (CVG-UGR) [21] databases. 4.1 Visual assessment Fig. 3 shows the bird image. The original figure is the dark image. HE method enhanced the image and lost the natural characteristics. HMF scheme enhanced the image up to some extent, but, overall the image does not provide very pleasant look. AGCWD shows very good contrast enhancement effect for this image with a clear background and foreground. ESIHE, GCAHM and PLTHE produced similar images and showed very less enhancement effect. The best visualisation is provided by the proposed method. Fig. 3Open in figure viewerPowerPoint 'Bird' image and the contrast enhancement results of different algorithms Fig. 4 shows the gate image. HE has again over enhanced the image. HMF enhanced the image well. The image generated by the AGCWD method is enhanced, but it gives dull look. ESIHE generated some noise in the background portion. GCAHM produced similar image to AGCWD. PLTHE showed very less enhancement effect. The proposed method enhanced the image very well with sharp and clear output. Fig. 4Open in figure viewerPowerPoint 'Gate' image and the contrast enhancement results of different algorithms Fig. 5 shows the result for a plane image. As it can be observed that the image generated by HE is completely distorted. HMF has enhanced the image, but it introduced distortion in the ground area. AGCWD also introduced too much distortion in the ground area. ESIHE does not show much enhancement effect for this image. GCAHM and PLTHE enhanced the image well. However, again the best result and image are generated by the proposed method. Fig. 5Open in figure viewerPowerPoint 'Plane' image and the contrast enhancement results of different algorithms Fig. 6 shows the girl's image. HE produced a noise and saturation artefact in the image. HMF has over brightened the image. AGCWD produced distortion in the image. ESIHE, GCAHM and PLTHE produced good enhancement results for this image. The proposed method enhanced the image very well with pleasant look. Fig. 6Open in figure viewerPowerPoint 'Girl' image and the contrast enhancement results of different algorithms Fig. 7 shows the result of the house image. HE again introduced noise in the image. It can be observed that for this image HMF, AGCWD, ESIHE, GCAHM and PLTHE, all methods generated similar kinds of images, but if we clearly observe house bricks are clearer in ESIHE method. The proposed method generated image with enhanced background and foreground. Fig. 7Open in figure viewerPowerPoint 'House' image and the contrast enhancement results of different algorithms Fig. 8 shows the window image. HE has produced a saturation artefact in the image. HMF, AGCWD and ESIHE produced almost similar outputs for this image with adequate amount of enhancement. GCAHM and PLTHE do not provide much enhancement. The proposed method generated enhanced image with very sharp features. Fig. 8Open in figure viewerPowerPoint 'Window' image and the contrast enhancement results of different algorithms Fig. 9 shows the airplane image. The image generated by the HE method is completely distorted. HMF also introduced noise in the image. AGCWD generated well enhanced image with clear sky as well as an airplane. ESIHE, GCAHM and PLTHE also created artefacts in the image. Image processed by proposed method is very smooth and clear with adequate amount of contrast enhancement. Fig. 9Open in figure viewerPowerPoint 'Airplane' image and the contrast enhancement results of different algorithms Fig. 10 shows the result for the seen image. HE has over enhanced the image. HMF enhanced the image well. AGCWD showed an enhancement in some areas while other areas remained dark. ESIHE enhanced the image, but less as compared to HMF. The image generated by GCAHM is very dull. PLTHE showed similar results to ESIHE. The proposed method has enhanced the overall image very well with a smooth and clear look. Fig. 10Open in figure viewerPowerPoint 'Seen' image and the contrast enhancement results of different algorithms Fig. 11 shows the result of the ruins image. HE distorted the image. HMF also introduced some noise in the background portion. AGCWD showed very less enhancement effect. ESIHE showed very good results for this image with clear sky and wall bricks. GCAHM and PLTHE also produced enhanced images but less as compared to ESIHE. The proposed method showed reasonably well-enhanced output with clear details. Fig. 11Open in figure viewerPowerPoint 'Ruins' image and the contrast enhancement results of different algorithms 4.2 Quantitative assessment Quantitative assessment is a necessary part of the image enhancement without which the validation of the method cannot be proved. Three metrics, such as entropy, QI and QRCM measurements are used here to compare the performance of the proposed method with others. 4.2.1 Entropy Entropy [14] is the measurement of information content present in the image. A high value of entropy means richer details provided by the image. Entropy is calculated with the help of PDF as (11)where is the probability value of the kth intensity level. Table 1 shows the results for various parameter measures. The highest values are bold and second-highest values are underlined. As we can observe from the table that proposed method provides highest entropy for bird, gate, plane, girl, window, airplane and seen images. For house, room and ruins images, ESIHE gives the highest entropy values. It can also be seen that the average entropy value is the highest for the proposed method. Table 1. Various parameter measures Metric IMG Input HE HMF AGCWD ESIHE GCAHM PLTHE Proposed entropy bird 7.06 5.96 7.42 7.78 7.08 7.05 7.01 7.80 gate 7.14 5.98 7.53 7.78 7.12 7.60 7.11 7.83 plane 6.64 5.86 6.89 6.60 6.62 7.07 6.58 7.49 girl 5.97 5.48 6.39 5.40 6.35 6.14 6.16 6.46 house 7.06 5.85 7.04 7.17 7.41 7.18 7.05 7.33 room 5.96 5.69 5.94 7.07 7.61 7.26 5.98 6.97 window 7.30 5.97 7.24 7.64 7.61 7.29 7.29 7.77 airplane 6.23 5.87 6.20 6.65 6.70 6.59 6.22 6.90 seen 5.65 5.71 5.98 5.67 5.80 5.60 5.80 7.08 ruins 6.40 5.86 6.85 6.81 7.27 6.95 6.93 7.05 average 6.54 5.82 6.74 6.85 6.95 6.87 6.61 7.26 Q.I. bird — 0.54 0.65 0.75 0.68 0.65 0.67 0.89 gate — 0.61 0.70 0.64 0.60 0.69 0.59 0.85 plane — 0.40 0.50 0.47 0.59 0.75 0.67 0.79 girl — 0.20 0.46 0.28 0.52 0.65 0.60 0.75 house — 0.46 0.70 0.75 0.72 0.79 0.77 0.86 room — 0.13 0.39 0.21 0.50 0.92 0.50 0.90 window — 0.69 0.75 0.86 0.80 0.75 0.78 0.95 airplane — 0.35 0.59 0.85 0.56 0.62 0.64 0.71 seen — 0.13 0.32 0.28 0.51 0.67 0.52 0.70 ruins — 0.36 0.50 0.64 0.88 0.78 0.84 0.78 average — 0.38 0.55 0.57 0.63 0.72 0.65 0.81 QRCM bird — 0.05 0.05 0.17 0.07 0.12 0.15 0.17 gate — 0.12 0.13 0.29 0.14 0.23 0.15 0.25 plane — 0.10 0.11 0.14 0.11 0.13 0.21 0.27 girl — 0.05 0.03 0.06 0.08 0.02 0.08 0.11 house — 0.03 0.03 0.08 0.13 0.02 0.10 0.06 room — 0.15 0.19 0.18 0.22 0.27 0.14 0.26 window — 0.14 0.13 0.11 0.14 0.08 0.08 0.16 airplane — 0.11 0.16 0.21 0.11 0.18 0.18 0.19 seen — 0.39 0.21 0.37 0.42 0.26 0.40 0.43 ruins — 0.13 0.25 0.17 0.32 0.18 0.21 0.27 average — 0.12 0.12 0.17 0.17 0.15 0.17 0.21 4.2.2 Quality index (QI) The second parameter tested here is QI [22] value. Image QI is evaluated for determining the amount of distortion present in an image. Basically, this parameter is calculated in reference to the original image. A higher value of QI means less distortion in the image. Let p = , where (i = 1, 2, …, N) and q = , where (i = 1, 2, …, N) be the original and enhanced images, respectively. The QI is defined as (12)where and . . As we can observe from Table 1 that expects room, airplane and ruins images proposed method provides the highest value of QI for other images. Also, the average value produced by the proposed method is the highest. 4.2.3 Quality-aware relative contrast measure QRCM [23] utilises the image pixel gradients to measure the image quality. It is further incorporated with two more weighting factors to generate the final score (13)where (14) (15) (16)where subscripts in and out represent the original and enhanced images, . (17) (18) (19)In a 3 × 3 patch, the derivatives are and in the vertical and horizontal directions, the gradient is given by , and QRCM [−1,1]. When QRCM is close to unity, it represents better image quality. As can be observed from Table 1 that for gate, room, airplane and ruins proposed method does not provide the highest value for QRCM, but for the rest of the images it gives best values compared to other methods. However, again the average value is the highest among all. 4.2.4 Structural similarity index measure The SSIM is a well-known quality metric used to measure the similarity between two images [24]. The SSIM is designed by modelling any image distortion as a combination of three factors that are loss of correlation, luminance distortion and contrast distortion. The SSIM is defined as where and are the mean luminance of two images. and are the standard deviations. The positive constants C1, C2 and C3 are used to avoid a null denominator. Table 2 shows the value of SSIM. As can be observed that for bird, gate and plane images the value of SSIM obtained by proposed method is the highest and for rest images it is less compared to one of the other methods. Again, the average value is the highest for both PLTHE and proposed method. Table 2. Various parameter measures Metric IMG HE HMF AGCWD ESIHE GCAHM PLTHE Proposed SSIM bird 0.21 0.65 0.68 0.72 0.75 0.93 0.94 gate 0.53 0.80 0.87 0.56 0.85 0.93 0.93 plane 0.36 0.68 0.52 0.70 0.95 0.84 0.96 girl 0.29 0.46 0.30 0.93 0.90 0.94 0.92 house 0.63 0.78 0.82 0.95 0.90 0.95 0.90 room 0.25 0.43 0.50 0.59 0.69 0.90 0.86 window 0.39 0.91 0.85 0.88 0.89 0.98 0.93 airplane 0.39 0.55 0.95 0.82 0.92 0.92 0.88 seen 0.14 0.36 0.60 0.27 0.75 0.62 0.69 ruins 0.41 0.72 0.56 0.83 0.79 0.93 0.81 average 0.36 0.63 0.66 0.72 0.83 0.89 0.89 CQE bird 0.18 0.20 0.45 0.22 0.42 0.52 0.62 gate 0.12 0.28 0.15 0.29 0.30 0.17 0.30 plane 0.18 0.35 0.22 0.41 0.50 0.57 0.65 girl 0.00 0.09 0.02 0.14 0.12 0.17 0.14 house 0.35 0.45 0.40 0.52 0.40 0.51 0.43 room 0.30 0.35 0.59 0.49 0.62 0.51 0.77 window 0.45 0.60 0.61 0.59 0.64 0.67 0.73 airplane 0.00 0.01 0.04 0.03 0.02 0.02 0.05 seen 0.40 0.58 0.49 0.69 0.65 0.69 0.75 ruins 0.11 0.18 0.22 0.27 0.25 0.23 0.26 average 0.20 0.30 0.31 0.36 0.39 0.40 0.47 4.2.5 Colour quality enhancement We have also calculated a parameter to check the colour quality [25] of images used in this paper. Basically, CQE is a no-reference quality assessment metric and is calculated by combining colorfulness, sharpness and contrast metrics linearly. It is given as (20)where = R−G and = 0.5(R + G)−B, colour components of a colour image. and are standard deviation of and , respectively. Similarly, and are their means. From Table 2, it can be observed that except girl, house and ruins images, the proposed method provides the highest CQE value for other images and also, the average value is the highest among all. Fig. 12 shows the average result of 30 images taken from the CVG-UGR database. The blue line shows the result for entropy. As it can be clearly observed that average entropy is highest for the proposed method. Fig. 12Open in figure viewerPowerPoint Average result of 30 images for different methods The violet line shows the result for CQE. Again the proposed method provides the highest average value for CQE. The green line gives the result for SSIM. The proposed method gives the highest average SSIM while PLTHE and GCAHM are on the second number. The red line provides the result for QRCM. As can be observed that there is very minute difference in the result of QRCM for different methods, but still the proposed method provides the highest average value. Similar are the results for QI, which is given by the dark blue line. 4.2.6 Computation complexity Table 3 compares the computation time complexity of the different methods. The experiments were conducted on a computer with Intel i5, 2.5 GHz processors, 4 GB RAM and Matlab 2015a. For comparison, we have taken the images of different sizes and all are colour images. As can be observed the HE method requires minimum time to process all the images as compared to other methods; it is because HE method does not apply any modification scheme and is a simple equalisation process. After HE, proposed method requires minimum time for processing the images. ESIHE method is also very close to proposed method. Rest all methods computation time is very high as compared to proposed method. Hence, the proposed method can be very well utilised in real-time applications due to its fast speed and good results, which are not obtained by the standard HE method. Table 3. Average computation time (in sec) Image size HE HMF AGCWD ESIHE GCAHM PLTHE Proposed 256 × 256 × 3 0.04 1.45 2.47 0.22 1.09 0.90 0.18 321 × 481 × 3 0.05 2.98 5.81 0.23 2.42 2.03 0.19 365 × 490 × 3 0.07 3.21 6.33 0.25 3.11 2.42 0.23 512 × 768 × 3 0.09 9.87 14.12 0.34 7.66 5.44 0.32 600 × 893 × 3 0.10 10.98 15.37 0.39 9.52 6.59 0.37 5 Conclusion In this paper, a simple heuristic adaptive HE algorithm is proposed for image contrast enhancement. Basically, PDF of the image is calculated. Then, an adaptive parameter is calculated based on the mean and maximum values of that PDF. Thereafter, PDF and CDF are modified by applying a threshold limit to that adaptive parameter. 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