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

Downloads

Download data is not yet available.

References

M. S. Moad, M. R. Kafi, and A. Khaldi, "Medical image watermarking for secure e-healthcare applications," Multimedia Tools and Applications, vol. 81, no. 30, pp. 44087–44107, Dec. 2022.

H. Wu, G. Liu, Y. Yao, and X. Zhang, "Watermarking Neural Networks With Watermarked Images," IEEE Transactions on Circuits and Systems for Video Technology, vol. 31, no. 7, pp. 2591–2601, Jul. 2021.

W.-W. Hu, R.-G. Zhou, A. El-Rafei, and S.-X. Jiang, "Quantum Image Watermarking Algorithm Based on Haar Wavelet Transform," IEEE Access, vol. 7, pp. 121303–121320, 2019.

A. Bose and S. P. Maity, "Secure sparse watermarking on DWT-SVD for digital images," Journal of Information Security and Applications, vol. 68, Aug. 2022, Art. no. 103255.

D. Li, X. Dai, J. Gui, J. Liu, and X. Jin, "A reversible watermarking for image content authentication based on wavelet transform," Signal, Image and Video Processing, vol. 18, no. 3, pp. 2799–2809, Apr. 2024.

A. Lakhani, N. Gupta, and A. Anand, "Enhancing robustness and security of medical images through composite watermarking method," Soft Computing, Jan. 2024.

Z. Tang, X. Chai, Y. Lu, B. Wang, and Y. Tan, "An end-to-end screen shooting resilient blind watermarking scheme for medical images," Journal of Information Security and Applications, vol. 76, Aug. 2023, Art. no. 103547.

X. Xia, S. Zhang, K. Wang, and T. Gao, "A novel color image tampering detection and self-recovery based on fragile watermarking," Journal of Information Security and Applications, vol. 78, Nov. 2023, Art. no. 103619.

J. A. P. Artiles, D. P. B. Chaves, and C. Pimentel, "Robust image watermarking algorithm using chaotic sequences," Journal of Information Security and Applications, vol. 68, Aug. 2022, Art. no. 103219

H. Fang et al., "A Camera Shooting Resilient Watermarking Scheme for Underpainting Documents," IEEE Transactions on Circuits and Systems for Video Technology, vol. 30, no. 11, pp. 4075–4089, Aug. 2020.

J. Li et al., "Single Exposure Optical Image Watermarking Using a cGAN Network," IEEE Photonics Journal, vol. 13, no. 2, pp. 1–11, Apr. 2021.

C. SaiTeja and J. B. Seventline, "A hybrid learning framework for multi-modal facial prediction and recognition using improvised non-linear SVM classifier," AIP Advances, vol. 13, no. 2, Feb. 2023, Art. no. 025316.

S. Chopparapu and J. B. Seventline, "An Efficient Multi-modal Facial Gesture-based Ensemble Classification and Reaction to Sound Framework for Large Video Sequences," Engineering, Technology & Applied Science Research, vol. 13, no. 4, pp. 11263–11270, Aug. 2023.

A. M. S. Saleh, "A Power-Aware Method for IoT Networks with Mobile Stations and Dynamic Power Management Strategy," Engineering, Technology & Applied Science Research, vol. 13, no. 6, pp. 12108–12114, Dec. 2023.

S. Zafar, G. Miraj, R. Baloch, D. Murtaza, and K. Arshad, "An IoT Based Real-Time Environmental Monitoring System Using Arduino and Cloud Service," Engineering, Technology & Applied Science Research, vol. 8, no. 4, pp. 3238–3242, Aug. 2018.

Downloads

How to Cite

[1]
Chopparapu, S., Chopparapu, G. and Vasagiri, D. 2024. Enhancing Visual Perception in Real-Time: A Deep Reinforcement Learning Approach to Image Quality Improvement. Engineering, Technology & Applied Science Research. 14, 3 (Jun. 2024), 14725–14731. DOI:https://doi.org/10.48084/etasr.7500.

Metrics

Abstract Views: 283
PDF Downloads: 340

Metrics Information

Most read articles by the same author(s)