Zhengbing Hu; Oksana Shkurat; Krzysztof Przystupa; Orest Kochan; Marharyta Ivakhnenko

Low-Light Image Enhancement Technology Based on Image Categorization, Processing and Retinex Deep Network

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Author(s)

Zhengbing Hu ¹ Oksana Shkurat ^2,* Krzysztof Przystupa ³ Orest Kochan ⁴ Marharyta Ivakhnenko ²

1. School of Computer Science, Hubei University of Technology, Wuhan, 430079, China

2. Department of Computer Systems Software, National Technical University of Ukraine “Kyiv Polytechnic Institute”, Kyiv, 03056, Ukraine

3. Department of Automation, Lublin University of Technology, Poland

4. Department of Information-Measuring Technologies, Lviv Polytechnic National University, Lviv, Ukraine

* Corresponding author.

DOI: https://doi.org/10.5815/ijigsp.2024.05.01

Received: 26 May 2024 / Revised: 21 Jun. 2024 / Accepted: 10 Jul. 2024 / Published: 8 Oct. 2024

Index Terms

Low-Light Image Enhancement, Image Contrast Enhancement, Retinex Deep Network, Image Processing, Image Knowledge Extraction

Abstract

Low-light scenes are characterized by the loss of illumination, the noise, the color distortion and serious information degradation. The low-light image enhancement is a significant part of computer vision technology. The low-light image enhancement methods aim to an image recover to a normal-light image from dark one, a noise-free image from a noisy one, a clear image from distorting one. In this paper, the low-light image enhancement technology based on Retinex-based deep network combined with the image processing-based module is proposed. The proposed technology combines the use of traditional and deep learning methodologies, designed within a simple yet efficient architectural framework that focuses on essential feature extraction. The proposed preprocessing module of low-light image enhancement is centered on the unique knowledge and features of an image. The choice of a color model and a technique of an image transformation depends on an image dynamic range to ensure high results in terms of transfer a color, detail integrity and overall visual quality. The proposed Retinex-based deep network has been trained and tested on transformed images by means of preprocessing module that leads to an effective supervised approach to low-light image enhancement and provide superior performance. The proposed preprocessing module is implemented as an independent image enhancement module in a computer system of an image analysis and as the component module in a neural network system of an image analysis. Experimental results on the low light paired dataset show that the proposed method can reduce noise and artifacts in low-light images, and can improve contrast and brightness, demonstrating its advantages. The proposed approach injects new ideas into low light image enhancement, providing practical applications in challenging low-light scenarios.

Cite This Paper

Zhengbing Hu, Oksana Shkurat, Krzysztof Przystupa, Orest Kochan, Marharyta Ivakhnenko, "Low-Light Image Enhancement Technology Based on Image Categorization, Processing and Retinex Deep Network", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.16, No.5, pp. 1-13, 2024. DOI:10.5815/ijigsp.2024.05.01

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