MTIE-Net: Multi-technology fusion of low-light image enhancement network

Author:

Tao JingORCID,Wu Hao,Ni Zhihao,Jin Zhongyang,Zhong Changhua

Abstract

Images obtained in low-light scenes are often accompanied by problems such as low visibility, blurred details, and color distortion, enhancing them can effectively improve the visual effect and provide favorable conditions for advanced visual tasks. In this study, we propose a Multi-Technology Fusion of Low-light Image Enhancement Network (MTIE-Net) that modularizes the enhancement task. MTIE-Net consists of a residual dense decomposition network (RDD-Net) based on Retinex theory, an encoder-decoder denoising network (EDD-Net), and a parallel mixed attention-based self-calibrated illumination enhancement network (PCE-Net). The low-light image is first decomposed by RDD-Net into a lighting map and reflectance map; EDD-Net is used to process noise in the reflectance map; Finally, the lighting map is fused with the denoised reflectance map as an input to PCE-Net, using the Fourier transform for illumination enhancement and detail recovery in the frequency domain. Numerous experimental results show that MTIE-Net outperforms the comparison methods in terms of image visual quality enhancement improvement, denoising, and detail recovery. The application in nighttime face detection also fully demonstrates its promise as a pre-processing means in practical applications.

Funder

The Project of Sichuan provincial science and Technology Department

The artificial intelligence key laboratory of Sichuan province Foundation

Enterprise informatization and Internet of things measurement and control technology key laboratory project of Sichuan provincial university

Youth and Middle-aged Scientific and Technological Innovation Leading Talents Program of the Corps

Publisher

Public Library of Science (PLoS)

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