Abstract: Computer vision has its numerous real-world applications on Visual Object Tracking which includes human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security, human-computer interaction, autonomous vehicles, robotics, motion-based recognition, video indexing, surveillance and security. The factors affecting the tracking process is due to low illumination, haze and cloudy environment and noisy environment. In this paper, we aim to extensively review the latest trends and advances in adaptive enhancement algorithm and evaluate the performance using Full reference like, SSIM (Structure Similarity Index Measure), MS-SSIM (Multi-scale Structure Similarity Index Measure), ESSIM (Edge Strength Structural Similarity Index), FSIM (Feature Similarity Index Measure), VIF (Visual Information Fidelity), CW-SSIM (complex wavelet structural similarity), UQI (Universal Quality Index), IEF (Image Enhancement Factor), IQI (Image Quality Index), EME (Enhancement Measurement Error), CVSI (Contrast and Visual Salient Information), MCSD (Multiscale contrast similarity deviation), NQM (Noise Quality Measure), Gradient Magnitude Similarity Mean (GMSM), Gradient Magnitude Similarity Deviation (GMSM) and no-reference image quality measures Perception based Image Quality Evaluator (PIQE), Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), Naturalness Image Quality Evaluator (NIQE), Average Gradient (AG), Contrast, Information Entropy (IE), Lightness order Error (LOE). The main purpose of adaptive image enhancement is to smooth the uniform area and sharpen the border of an image to improve its visual quality. In this paper, fourteen image enhancement algorithms were tested on LoL dataset to benchmark the time taken to process them and their output quality was evaluated. Results from this study will give insights to image analysts for selecting image enhancement algorithms which acts as a pre- processing stage for Visual object Tracking.

Jenita Subash and Jharna Majumdar, “Comparison of Image Enhancement Algorithms for Improving the Visual Quality in Computer Vision Application” International Journal of Advanced Computer Science and Applications(IJACSA), 13(7), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130775

@article{Subash2022,
title = {Comparison of Image Enhancement Algorithms for Improving the Visual Quality in Computer Vision Application},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130775},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130775},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {7},
author = {Jenita Subash and Jharna Majumdar}
}