Bilateral Histogram Equalization for Contrast Enhancement

2016 ◽  
Vol 4 (4) ◽  
pp. 15-34 ◽  
Author(s):  
Feroz Mahmud Amil ◽  
Shanto Rahman ◽  
Md. Mostafijur Rahman ◽  
Emon Kumar Dey
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
State Of The Art ◽  
Histogram Equalization ◽  
Harmonic Mean ◽  
New Technique ◽  
Qualitative And Quantitative ◽  
Quantitative Manner ◽  
A New Technique

As image enhancement is a well discussed issue, various methods have already been proposed till to date. Some of these methods perform well for specific applications but most of the techniques suffer from artifacts due to the over or under enhancement. To mitigate this problem a new technique namely Bilateral Histogram Equalization for contrast enhancement (BHE) which uses Harmonic mean of the image to divide the histogram is introduced. BHE is evaluated in both qualitative and quantitative manner and the results show that BHE creates less artifacts on several standard images than other existing state-of-the-art image enhancement techniques.

Bilateral Histogram Equalization for Contrast Enhancement

2018 ◽  
pp. 1224-1244
Author(s):  
Feroz Mahmud Amil ◽  
Shanto Rahman ◽  
Md. Mostafijur Rahman ◽  
Emon Kumar Dey
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
State Of The Art ◽  
Histogram Equalization ◽  
Harmonic Mean ◽  
New Technique ◽  
Qualitative And Quantitative ◽  
Quantitative Manner ◽  
A New Technique

As image enhancement is a well discussed issue, various methods have already been proposed till to date. Some of these methods perform well for specific applications but most of the techniques suffer from artifacts due to the over or under enhancement. To mitigate this problem a new technique namely Bilateral Histogram Equalization for contrast enhancement (BHE) which uses Harmonic mean of the image to divide the histogram is introduced. BHE is evaluated in both qualitative and quantitative manner and the results show that BHE creates less artifacts on several standard images than other existing state-of-the-art image enhancement techniques.

scholarly journals Noise Suppression and Contrast Enhancement via Bayesian Residual Transform (BRT) in Low-Light Conditions

2016 ◽  
Vol 2 (1) ◽  
Author(s):  
Audrey G. Chung ◽  
Alexander Wong
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Noise Suppression ◽  
State Of The Art ◽  
Light Conditions ◽  
Low Light ◽  
Preliminary Results ◽  
Denoised Image

Very low-light conditions are problematic for current robotic visionalgorithms as captured images are subject to high levels of ISOnoise. We propose a Bayesian Residual Transform (BRT) model forjoint noise suppression and image enhancement for images capturedunder these low-light conditions via a Bayesian-based multiscaleimage decomposition. The BRT models a given image as thesum of residual images, and the denoised image is reconstructedusing a weighted summation of these residual images. We evaluatethe efficacy of the proposed BRT model using the VIP-LowLightdataset, and preliminary results show a notable visual improvementover state-of-the-art denoising methods.

scholarly journals Contrast and Synthetic Multiexposure Fusion for Image Enhancement

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Marwan Ali Albahar
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Image Enhancement ◽  
State Of The Art ◽  
Input Image ◽  
Synthetic Image ◽  
Improve Image Quality ◽  
Exposure Fusion ◽  
Novel Technique ◽  
Lighting Conditions

Many hardware and software advancements have been made to improve image quality in smartphones, but unsuitable lighting conditions are still a significant impediment to image quality. To counter this problem, we present an image enhancement pipeline comprising synthetic multi-image exposure fusion and contrast enhancement robust to different lighting conditions. In this paper, we propose a novel technique of generating synthetic multi-exposure images by applying gamma correction to an input image using different values according to its luminosity for generating multiple intermediate images, which are then transformed into a final synthetic image by applying contrast enhancement. We observed that our proposed contrast enhancement technique focuses on specific regions of an image resulting in varying exposure, colors, and details for generating synthetic images. Visual and statistical analysis shows that our method performs better in various lighting scenarios and achieves better statistical naturalness and discrete entropy scores than state-of-the-art methods.

scholarly journals A Comparative Study of Semantic Segmentation Models for Identification of Grape with Different Varieties

Agriculture ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 997
Author(s):  
Yun Peng ◽  
Aichen Wang ◽  
Jizhan Liu ◽  
Muhammad Faheem
Keyword(s):  
Image Enhancement ◽  
Precision Agriculture ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Histogram Equalization ◽  
Performance Difference ◽  
Convolutional Network ◽  
Enhancement Method ◽  
The Impact ◽  
Segmentation Models

Accurate fruit segmentation in images is the prerequisite and key step for precision agriculture. In this article, aiming at the segmentation of grape cluster with different varieties, 3 state-of-the-art semantic segmentation networks, i.e., Fully Convolutional Network (FCN), U-Net, and DeepLabv3+ applied on six different datasets were studied. We investigated: (1) the segmentation performance difference of the 3 studied networks; (2) The impact of different input representations on segmentation performance; (3) The effect of image enhancement method to improve the poor illumination of images and further improve the segmentation performance; (4) The impact of the distance between grape clusters and camera on segmentation performance. The experiment results show that compared with FCN and U-Net the DeepLabv3+ combined with transfer learning is more suitable for the task with an intersection over union (IoU) of 84.26%. Five different input representations, namely RGB, HSV, L*a*b, HHH, and YCrCb obtained different IoU, ranging from 81.5% to 88.44%. Among them, the L*a*b got the highest IoU. Besides, the adopted Histogram Equalization (HE) image enhancement method could improve the model’s robustness against poor illumination conditions. Through the HE preprocessing, the IoU of the enhanced dataset increased by 3.88%, from 84.26% to 88.14%. The distance between the target and camera also affects the segmentation performance, no matter in which dataset, the closer the distance, the better the segmentation performance was. In a word, the conclusion of this research provides some meaningful suggestions for the study of grape or other fruit segmentation.

scholarly journals Fuzzy Logic-Based Histogram Equalization for Image Contrast Enhancement

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
V. Magudeeswaran ◽  
C. G. Ravichandran
Keyword(s):  
Fuzzy Logic ◽  
Contrast Enhancement ◽  
Histogram Equalization ◽  
Image Contrast ◽  
Experimental Result ◽  
Natural Image ◽  
Original Image ◽  
Qualitative And Quantitative ◽  
Image Contrast Enhancement ◽  
Fuzzy Histogram

Fuzzy logic-based histogram equalization (FHE) is proposed for image contrast enhancement. The FHE consists of two stages. First, fuzzy histogram is computed based on fuzzy set theory to handle the inexactness of gray level values in a better way compared to classical crisp histograms. In the second stage, the fuzzy histogram is divided into two subhistograms based on the median value of the original image and then equalizes them independently to preserve image brightness. The qualitative and quantitative analyses of proposed FHE algorithm are evaluated using two well-known parameters like average information contents (AIC) and natural image quality evaluator (NIQE) index for various images. From the qualitative and quantitative measures, it is interesting to see that this proposed method provides optimum results by giving better contrast enhancement and preserving the local information of the original image. Experimental result shows that the proposed method can effectively and significantly eliminate washed-out appearance and adverse artifacts induced by several existing methods. The proposed method has been tested using several images and gives better visual quality as compared to the conventional methods.

scholarly journals A Novel Technique for Automatic Image Enhancement using HTHET Approach

2019 ◽  
Vol 8 (1) ◽  
pp. 26-31
Author(s):  
V. Murali ◽  
T. Venkateswarlu
Keyword(s):  
Contrast Enhancement ◽  
Image Enhancement ◽  
Information Content ◽  
Optimization Method ◽  
Histogram Equalization ◽  
Original Image ◽  
Enhancement Technique ◽  
Novel Technique ◽  
Equalization Technique ◽  
Histogram Transformation

Image enhancement techniques are methods used for producing images with better quality than the original image. None of the existing methods increase the information content of the image, and are usually of little interest for subsequent automatic analysis of images. In this paper, automated Image Enhancement is achieved by carrying out Histogram techniques. Histogram equalization (HE) is a spatial domain image enhancement technique, which effectively enhances the contrast of an image. We make use of Transformation and Hyperbolization techniques for automatic image enhancement. However, while it takes care of contrast enhancement, a modified histogram equalization technique, Histogram Transformation and Hyperbolization Equalization Technique (HTHET) using optimization method is proposed using EQHIST and LINHIST.

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