AR-MANet: A Low-Quality Image Restoration Method Based on Multi-Feature Fusion
Year: 2025
Type: article
Abstract: Exposure problems often affect image quality more significantly than noise and blurring, seriously affecting their effectiveness in computer vision applications. To address this core problem, this paper proposes an innovative deep-learning method. The method uses pyramidal multi-scale decomposition to repair the image layer by layer, taking care of both overexposure and underexposure problems. On this basis, we improve the U-Net model by introducing the Strengthen-Operate-Subtract (SOS) feature enhancement module and a multi-scale aggregate back-projection module to achieve finer image restoration and improve the propagation of feature information. In addition, we introduce an atrous residual dense block at the highest scale to expand the receptive field of the network and efficiently capture local information to improve the quality of the exposure-corrected images. These innovations significantly optimize the effect of image exposure correction and improve the problems of loss of image details, oversaturation of regional contrast, and imbalance of color naturalness in existing techniques. Through extensive and in-depth analytical evaluations, we have validated the effectiveness of our model, which also demonstrates excellent performance compared with the most recent methods on the test database.
Source: IEEE Signal Processing Letters
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4411336543