Dual-Camera Joint Deblurring-Denoising

TL;DR

This paper introduces a dual-camera approach that combines synchronized short and long exposure images to improve low-light image quality through denoising, deblurring, and fusion, achieving state-of-the-art results.

Contribution

It presents a novel dual-camera method leveraging synchronized burst and long exposure images for enhanced image denoising and deblurring, with fewer training parameters.

Findings

Achieves state-of-the-art results on synthetic datasets.

Outperforms competing methods on real dual-camera captures.

Uses five times fewer training parameters.

Abstract

Recent image enhancement methods have shown the advantages of using a pair of long and short-exposure images for low-light photography. These image modalities offer complementary strengths and weaknesses. The former yields an image that is clean but blurry due to camera or object motion, whereas the latter is sharp but noisy due to low photon count. Motivated by the fact that modern smartphones come equipped with multiple rear-facing camera sensors, we propose a novel dual-camera method for obtaining a high-quality image. Our method uses a synchronized burst of short exposure images captured by one camera and a long exposure image simultaneously captured by another. Having a synchronized short exposure burst alongside the long exposure image enables us to (i) obtain better denoising by using a burst instead of a single image, (ii) recover motion from the burst and use it for motion-aware deblurring of the long exposure image, and (iii) fuse the two results to further enhance quality. Our method is able to achieve state-of-the-art results on synthetic dual-camera images from the GoPro dataset with five times fewer training parameters compared to the next best method. We also show that our method qualitatively outperforms competing approaches on real synchronized dual-camera captures.