Multi-Reference Image Super-Resolution: A Posterior Fusion Approach

• URL: http://arxiv.org/abs/2212.09988v1
• Date: Tue, 20 Dec 2022 04:15:03 GMT
• Title: Multi-Reference Image Super-Resolution: A Posterior Fusion Approach
• Authors: Ke Zhao, Haining Tan, Tsz Fung Yau
• Abstract summary: This paper proposes a 2-step-weighting posterior fusion approach to combine the outputs of RefSR models with multiple references. 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.
• Score: 0.3867363075280544
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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