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.

提出了一种用于多摄像头智能手机的模糊处理框架，利用混合成像技术，通过同时捕捉来自智能手机的长曝光广角图像和超广角连拍图像，并使用清晰的连拍图像估计模糊核进行广角图像的去模糊处理。使用创建的 HCBlur 数据集进行网络的学习和评估，该数据集包含模糊的广角图像、清晰的超广角连拍图像以及它们的清晰广角图像对应物。通过广泛的评估，结果显示出了现有技术的高质量。