EDMFEN: Edge detection-based multi-scale feature enhancement Network for low-light image enhancement

• Canlin Li,
• Shun Song,
• Pengcheng Gao,
• Wei Huang,
• Lihua Bi,

Vol. 18, No. 4, April 30, 2024
10.3837/tiis.2024.04.009, Download Paper (Free):

• Low-Light Image Enhancement
• Deep Learning
• Edge detection
• multi-scale feature enhancement
• spatial attention mechanism.

Abstract

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the state-of-the-art LLIE methods.

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