Learning From Multi-Perception Features for Real-Word Image Super-Resolution

Actual image super-resolution is an extremely challenging task due to complex degradations existing in the image. To solve this problem, two dominant methodologies have emerged: degradation-estimation-based Addressing actual image super-resolution remains a formidable challenge due to the intricate degradations present in images. Two primary methodologies have emerged: degradation-estimation-based and blind-based methods. The former often struggle to accurately estimate degradation, limiting their effectiveness on real low-resolution images. Conversely, blind-based methods rely on a single perceptual perspective, constraining their adaptability to diverse perceptual characteristics. In response to these challenges, we present MPF-Net, a novel super-resolution approach aimed at enhancing real-world image super-resolution tasks by enabling the model to learn multiple perceptual features from input images. Our method features a Multi-Perception Feature Extraction module (MPFE) designed to extract diverse perceptual details, complemented by Cross-Perception Blocks (CPB) facilitating the fusion of this information for efficient super-resolution reconstruction. Additionally, we introduce a contrastive regularization term (CR) to enhance the model’s learning by leveraging newly generated HR and LR images as positive and negative samples. Experimental results on challenging real-world SR datasets demonstrate the superiority of our approach over existing state-of-the-art methods, both qualitatively and quantitatively.