Non-linear weight adjustment in adaptive gamma correction for image contrast enhancement
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Non-linear weight adjustment in adaptive gamma correction for image contrast enhancement Debapriya Sengupta1,2
· Arindam Biswas1 · Phalguni Gupta3
Received: 6 September 2019 / Revised: 5 August 2020 / Accepted: 11 August 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Image enhancement remains an intricate problem, crucial for image analysis. Several algorithms exist for the same. A few among these algorithms categorize images into different classes based on their statistical parameters and apply separate enhancement functions for each class. One such algorithm is the well-known adaptive gamma correction (AGC) algorithm. It works well for each class of images, but fails when the statistical parameters lie on the boundary of separation of two classes. We have developed an enhancement algorithm which can enhance images which lie on the boundary of separation equally well, as images which lie deep inside the boundary. The basic idea behind the algorithm is to combine the different enhancement functions of AGC using non-linear weight adjustments. Both contrast and brightness have been modified using these weight adjustments. We have conducted experiments on a data-set consisting of 9979 images. Results show that by using the proposed algorithm, average entropy of the enhanced images increases by 3.97% and average root mean square (rms) increases by 14.29% over AGC. Visual improvement is also perceivable. Keywords Image enhancement · Adaptive gamma correction · Non-linear weight adjustment · Steepness parameter · Contrast enhancement · Brightness adjustment
Debapriya Sengupta
debapriya [email protected] Arindam Biswas [email protected] Phalguni Gupta [email protected] 1
Indian Institute of Engineering Science and Technology, Howrah, India
2
National Institute of Technical Teachers’ Training and Research, Kolkata, India
3
GLA University, Mathura, India
Multimedia Tools and Applications
1 Introduction Image enhancement is one of the most common operations in the domain of digital image processing. De-noising [9, 41], brightness enhancement [2], contrast enhancement [51], sharpness enhancement [55], tonal adjustment ([28]), resolution enhancement [1, 8, 18], all come under the umbrella of image enhancement. Enhancement algorithms vary according to the type of enhancement required. For example, noise removal algorithms only remove noise and do not improve brightness or contrast, whereas contrast enhancement algorithms improve overall contrast of the image, without removing noise or enhancing sharpness. Image enhancement plays crucial role in medical imaging, satellite imaging, remote sensing, surveillance imaging, video processing etc. There exist many well-known image enhancement algorithms. Some algorithms cater to specific problems in specific type of images, while others are general purpose algorithms, suitable for a wide variety of images. For example, [33] proposed a wavelet based algorithm for de-spiking motion artifacts, especially due to head movement, in resting stat
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