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OptiRet-Net: An Optimized Low-Light Image Enhancement Technique for CV-Based Applications in Resource-Constrained Environments

Published: 13 December 2024 Publication History

Abstract

The illumination of images can significantly impact computer-vision applications such as image classification, multiple object detection, and tracking, leading to a significant decline in detection and tracking accuracy. Recent advancements in deep learning techniques have been applied to Low-Light Image Enhancement (LLIE) to combat this issue. Retinex theory-based methods following a decomposition-adjustment pipeline for LLIE have performed well in various aspects. Despite their success, current research on Retinex-based deep learning still needs to improve in terms of optimization techniques and complicated convolution connections, which can be computationally intensive for end-device deployment. We propose an Optimized Retinex-Based CNN (OptiRet-Net) deep learning framework to address these challenges for the LLIE problem. Our results demonstrate that the proposed method outperforms existing state-of-the-art models in terms of full reference metrics with a PSNR of 21.87, SSIM of 0.80, LPIPS of 0.16, and zero reference metrics with a NIQE of 3.4 and PIQE of 56.6. Additionally, we validate our approach using a comprehensive evaluation comprising five datasets and nine prior methods. Furthermore, we assess the efficacy of our proposed model combining low-light multiple object tracking applications using YOLOX and ByteTrack in Versatile Video Coding (VVC/H.266) across various quantization parameters. Our findings reveal that LLIE-enhanced frames surpass their tracking results with a MOTA of 80.6% and a remarkable precision rate of 96%. Our model also achieves minimal file sizes by effectively compressing the enhanced low-light images while maintaining their quality, making it suitable for resource-constrained environments where storage or bandwidth limitations are a concern.

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      cover image ACM Transactions on Intelligent Systems and Technology
      ACM Transactions on Intelligent Systems and Technology  Volume 15, Issue 6
      December 2024
      727 pages
      EISSN:2157-6912
      DOI:10.1145/3613712
      • Editor:
      • Huan Liu
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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 13 December 2024
      Online AM: 12 October 2024
      Accepted: 23 September 2024
      Revised: 26 August 2024
      Received: 25 October 2023
      Published in TIST Volume 15, Issue 6

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      1. low-light image enhancement
      2. Multiple object tracking
      3. Video compression
      4. VVC
      5. ByteTrack
      6. YOLOX
      7. CNN models
      8. Retinex Theory

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