Burst Image Super-Resolution with Base Frame Selection

TL;DR

This paper introduces a new dataset and a frame selection network for burst image super-resolution with non-uniform exposures, improving image quality in real-world scenarios by selecting optimal frames for fusion.

Contribution

The work presents a novel benchmark dataset NEBI and a plug-and-play Frame Selection Network tailored for non-uniform burst exposures in super-resolution tasks.

Findings

The non-uniform exposure setting enhances real-world super-resolution performance.

The proposed FSN improves image quality with low computational overhead.

Experimental results validate the effectiveness of the NEBI dataset and FSN.

Abstract

Burst image super-resolution has been a topic of active research in recent years due to its ability to obtain a high-resolution image by using complementary information between multiple frames in the burst. In this work, we explore using burst shots with non-uniform exposures to confront real-world practical scenarios by introducing a new benchmark dataset, dubbed Non-uniformly Exposed Burst Image (NEBI), that includes the burst frames at varying exposure times to obtain a broader range of irradiance and motion characteristics within a scene. As burst shots with non-uniform exposures exhibit varying levels of degradation, fusing information of the burst shots into the first frame as a base frame may not result in optimal image quality. To address this limitation, we propose a Frame Selection Network (FSN) for non-uniform scenarios. This network seamlessly integrates into existing super-resolution methods in a plug-and-play manner with low computational costs. The comparative analysis reveals the effectiveness of the nonuniform setting for the practical scenario and our FSN on synthetic-/real- NEBI datasets.