Literature DB >> 34372222

Low-Light Image Enhancement Based on Multi-Path Interaction.

Bai Zhao1, Xiaolin Gong1, Jian Wang2,3, Lingchao Zhao1.   

Abstract

Due to the non-uniform illumination conditions, images captured by sensors often suffer from uneven brightness, low contrast and noise. In order to improve the quality of the image, in this paper, a multi-path interaction network is proposed to enhance the R, G, B channels, and then the three channels are combined into the color image and further adjusted in detail. In the multi-path interaction network, the feature maps in several encoding-decoding subnetworks are used to exchange information across paths, while a high-resolution path is retained to enrich the feature representation. Meanwhile, in order to avoid the possible unnatural results caused by the separation of the R, G, B channels, the output of the multi-path interaction network is corrected in detail to obtain the final enhancement results. Experimental results show that the proposed method can effectively improve the visual quality of low-light images, and the performance is better than the state-of-the-art methods.

Entities:  

Keywords:  color channel; convolutional neural network; low-light image; multi-path interaction

Year:  2021        PMID: 34372222     DOI: 10.3390/s21154986

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  2 in total

1.  Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior.

Authors:  Lingli Guo; Zhenhong Jia; Jie Yang; Nikola K Kasabov
Journal:  Sensors (Basel)       Date:  2021-12-23       Impact factor: 3.576

2.  Low-Light Image Enhancement via Retinex-Style Decomposition of Denoised Deep Image Prior.

Authors:  Xianjie Gao; Mingliang Zhang; Jinming Luo
Journal:  Sensors (Basel)       Date:  2022-07-26       Impact factor: 3.847

  2 in total

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