AR-MANet: A Low-Quality Image Restoration Method Based on Multi-Feature Fusion

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, handling both overexposure and underexposure problems. Firstly, we introduce the Strengthen-Operate-Subtract (SOS) feature enhancement module, which refines and enhances the image by building previously estimated images to make the exposure correction effect more pronounced. Additionally, we propose using the Multi-scale Aggregation Back-projection (MAB) module to improve the U-Net model. Through the error feedback mechanism, the features of non-adjacent layers are compensated and integrated effectively, and the loss of detailed information is reduced. We introduce a dense block of matte residuals at the maximum scale to expand the network's receptive field and effectively capture local information. These innovations significantly enhance image exposure correction, reducing detail loss, regional contrast saturation, and color imbalance. Extensive evaluations confirm the effectiveness of our model, which demonstrates superior performance across all datasets, particularly in PSNR, achieving an average value of 26.271.