Year: 2025
Type: article
Abstract: ABSTRACT The super‐resolution (SR) task aims to reconstruct high‐resolution content from low‐resolution images, and its core challenge is to solve the problem of ill‐posedness while balancing the fidelity and perceptual quality of the generated images. Although flow‐based SR models have made significant progress in modelling high‐resolution image distributions, their generated images still have shortcomings in terms of detail representation. To address the issues above, this paper proposes an optimised method that incorporates a conditionally learnt prior (latent module). Specifically, a Dual‐Dimensional Feature Co‐Enhancement (DFCE) module is developed to perform joint optimisation on the channel and spatial dimensions of the features. The experimental results on the public datasets show that this framework effectively improves the generation quality of images with almost no increase in the amount of computation. Furthermore, this framework can be seamlessly integrated into fixed‐scale and arbitrary‐scale streaming models without the need to modify their pre‐training weights or architecture, which provides efficient and flexible solutions for practical applications.
Source: IET Image Processing
DOI: 10.1049/ipr2.70159
Authors: Zhengjie Wei, Xiaomin Yang
Institutions: Nanyang Technological University, Sichuan University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,740
Funder: Sichuan University
Award: G8575156386
OpenAlex ID: W4412663792