Abstract

Mobile cameras, despite their significant advancements, still face low-light challenges due to compact sensors and lenses, leading to longer exposures and motion blur. Traditional solutions like blind deconvolution and learning-based methods often fall short in handling ill-posedness of the deblurring problem. To address this, we propose a novel deblurring framework for multi-camera smartphones, utilizing a hybrid imaging technique. We simultaneously capture a long exposure wide-angle image and ultra-wide burst images from a smartphone, and use the sharp burst to estimate blur kernels for deblurring the wide-angle image. For learning and evaluation of our network, we introduce the HCBlur dataset, which includes pairs of blurry wide-angle and sharp ultra-wide burst images, and their sharp wide-angle counterparts. We extensively evaluate our method, and the result shows the state-of-the-art quality.