arXiv:2212.09988
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach
원문 초록
Reference-based Super-resolution (RefSR) approaches have recently been proposed to overcome the ill-posed problem of image super-resolution by providing additional information from a high-resolution image. Multi-reference super-resolution extends this approach by allowing more information to be incorporated. This paper proposes a 2-step-weighting posterior fusion approach to combine the outputs of RefSR models with multiple references. Extensive experiments on the CUFED5 dataset demonstrate that the proposed methods can be applied to various state-of-the-art RefSR models to get a consistent improvement in image quality.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
A Tree-guided CNN for image super-resolution
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution
Trustworthy Image Super-Resolution via Generative Pseudoinverse
토론
이 논문과 연결된 포럼 글이 아직 없습니다.