  Currently, there are two popular approaches for addressing real-world image
super-resolution problems: degradation-estimation-based and blind-based
methods. However, degradation-estimation-based methods may be inaccurate in
estimating the degradation, making them less applicable to real-world LR
images. On the other hand, blind-based methods are often limited by their fixed
single perception information, which hinders their ability to handle diverse
perceptual characteristics. To overcome this limitation, we propose a novel SR
method called MPF-Net that leverages multiple perceptual features of input
images. Our method incorporates a Multi-Perception Feature Extraction (MPFE)
module to extract diverse perceptual information and a series of newly-designed
Cross-Perception Blocks (CPB) to combine this information for effective
super-resolution reconstruction. Additionally, we introduce a contrastive
regularization term (CR) that improves the model's learning capability by using
newly generated HR and LR images as positive and negative samples for ground
truth HR. Experimental results on challenging real-world SR datasets
demonstrate that our approach significantly outperforms existing
state-of-the-art methods in both qualitative and quantitative measures.
