A Systematic Literature Review on SOTA Machine learning-supported Computer Vision Approaches to Image Enhancement

Abstract

Image enhancement as a problem-oriented process of optimizing visual appearances to provide easier-toprocess input to automated image processing techniques is an area that will consistently be a companion to computer vision despite advances in image acquisition and its relevance continues to grow. For our systematic literature review, we consider the major peer-reviewed journals and conference papers on the state of the art in machine learning-based computer vision approaches for image enhancement. We describe the image enhancement methods relevant to our work and introduce the machine learning models used. We then provide a comprehensive overview of the different application areas and formulate research gaps for future scientific work on machine learning based computer vision approaches for image enhancement based on our results

Fine Tuning of Interval Configuration for Deep Reinforcement Learning Based Congestion Control

“…The current state-of-the-art [5][6][7][8][9][10][11] leverages the power of reinforcement learning. Reinforcement learning is a variant of machine learning [12]. It improves the adaptability of the congestion control algorithm to face the challenge of modern computer networks.…”