Learning from Multi-Perception Features for Real-Word Image Super-resolution

Axi Niu, Kang Zhang, Trung X. Pham, Pei Wang, Jinqiu Sun, In So Kweon, Yanning Zhang

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Transactions on Circuits and Systems for Video Technology
DOIs
StateAccepted/In press - 2024

Keywords

  • Contrastive Regularization
  • Cross-Perception Block
  • Degradation
  • Feature extraction
  • Image reconstruction
  • Image resolution
  • Kernel
  • Multi-Perception Feature Extraction
  • Real-world Image Super-resolution
  • Superresolution
  • Task analysis

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