Reference-based Motion Blur Removal: Learning to Utilize Sharpness in the Reference Image

TL;DR

This paper introduces a novel reference-based motion blur removal technique that leverages local patch matching and feature fusion to effectively deblur images using various reference images, including those from different scenes.

Contribution

It proposes a flexible method that utilizes reference images without strict assumptions, enhancing deblurring by matching patches and fusing features, compatible with existing single-image deblurring networks.

Findings

Effective in using diverse reference images for deblurring

Improves deblurring quality over single-image methods

Compatible with existing deblurring networks

Abstract

Despite the recent advancement in the study of removing motion blur in an image, it is still hard to deal with strong blurs. While there are limits in removing blurs from a single image, it has more potential to use multiple images, e.g., using an additional image as a reference to deblur a blurry image. A typical setting is deburring an image using a nearby sharp image(s) in a video sequence, as in the studies of video deblurring. This paper proposes a better method to use the information present in a reference image. The method does not need a strong assumption on the reference image. We can utilize an alternative shot of the identical scene, just like in video deblurring, or we can even employ a distinct image from another scene. Our method first matches local patches of the target and reference images and then fuses their features to estimate a sharp image. We employ a patch-based feature matching strategy to solve the difficult problem of matching the blurry image with the sharp reference. Our method can be integrated into pre-existing networks designed for single image deblurring. The experimental results show the effectiveness of the proposed method.