Image Contrast Enhancement based on Non-linear Processing

2016 ◽  
Author(s):  
Tomio Goto ◽  
Satoshi Hirano ◽  
Masaru Sakurai
Keyword(s):  
Contrast Enhancement ◽  
Image Contrast ◽  
Image Contrast Enhancement ◽  
Non Linear

A Review on Image Contrast Enhancement in Colored images

2019 ◽  
Vol 7 (4) ◽  
pp. 263-273
Author(s):  
Avadhesh Kumar Dixit ◽  
Rakesh Kumar Yadav
Keyword(s):  
Contrast Enhancement ◽  
Image Contrast ◽  
Image Contrast Enhancement

Learning Dual Transformation Networks for Image Contrast Enhancement

2020 ◽  
Vol 27 ◽  
pp. 1999-2003
Author(s):  
Yurui Zhu ◽  
Xueyang Fu ◽  
Aiping Liu
Keyword(s):  
Contrast Enhancement ◽  
Image Contrast ◽  
Dual Transformation ◽  
Image Contrast Enhancement

DCT Coefficients Weighting (DCTCW)-Based Gray Wolf Optimization (GWO) for Brightness Preserving Image Contrast Enhancement

2021 ◽  
Author(s):  
Saorabh Kumar Mondal ◽  
Arpitam Chatterjee ◽  
Bipan Tudu
Keyword(s):  
Contrast Enhancement ◽  
Objective Evaluation ◽  
Brightness Distribution ◽  
Image Contrast ◽  
Optimization Techniques ◽  
Biological Behavior ◽  
Gray Wolf ◽  
Original Image ◽  
Image Characteristics ◽  
Image Contrast Enhancement

Image contrast enhancement (CE) is a frequent image enhancement requirement in diverse applications. Histogram equalization (HE), in its conventional and different further improved ways, is a popular technique to enhance the image contrast. The conventional as well as many of the later versions of HE algorithms often cause loss of original image characteristics particularly brightness distribution of original image that results artificial appearance and feature loss in the enhanced image. Discrete Cosine Transform (DCT) coefficient mapping is one of the recent methods to minimize such problems while enhancing the image contrast. Tuning of DCT parameters plays a crucial role towards avoiding the saturations of pixel values. Optimization can be a possible solution to address this problem and generate contrast enhanced image preserving the desired original image characteristics. Biological behavior-inspired optimization techniques have shown remarkable betterment over conventional optimization techniques in different complex engineering problems. Gray wolf optimization (GWO) is a comparatively new algorithm in this domain that has shown promising potential. The objective function has been formulated using different parameters to retain original image characteristics. The objective evaluation against CEF, PCQI, FSIM, BRISQUE and NIQE with test images from three standard databases, namely, SIPI, TID and CSIQ shows that the presented method can result in values up to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the stated metrics which are competitive to the reported conventional and improved techniques. This paper can be considered a first-time application of GWO towards DCT-based image CE.

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