Enhancing Visual Perception in Real-Time: A Deep Reinforcement Learning Approach to Image Quality Improvement

Authors

  • SaiTeja Chopparapu Department of ECE, St. Peter’s Engineering College, Hyderabad, Telangana, India
  • Gowthami Chopparapu Department of CSE, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur, Andhra Pradesh, India
  • Divija Vasagiri Engineering SCIENCE-DS, SUNY Buffalo, Buffalo, NY, USA
Volume: 14 | Issue: 3 | Pages: 14725-14731 | June 2024 | https://doi.org/10.48084/etasr.7500

Abstract

In this paper, a novel approach to enhance image quality in real-time using Deep Reinforcement Learning (DRL) is introduced. The adopted method utilizes a Convolutional Neural Network (CNN) within a Q-learning framework to dynamically apply various image enhancement filters. These filters are selected based on their impact on the Structural Similarity Index Measure (SSIM), which serves as the primary metric for evaluating enhancements. The effectiveness of the proposed approach is demonstrated through extensive experiments, where improvements in image quality are measured by employing metrics such as SSIM, Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The results exhibit a significant potential for DRL in automating complex image-processing tasks in various real-world applications.

Keywords:

digital image processing, face recognition system, machine learning

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How to Cite

[1]
S. Chopparapu, G. Chopparapu, and D. Vasagiri, “Enhancing Visual Perception in Real-Time: A Deep Reinforcement Learning Approach to Image Quality Improvement”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14725–14731, Jun. 2024.

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