LSD$_2$ -- Joint Denoising and Deblurring of Short and Long Exposure Images with CNNs
This addresses the problem of capturing clear photos in low-light with motion blur for smartphone users, representing an incremental improvement over existing fusion techniques.
The paper tackles low-light photography with handheld smartphone cameras by fusing short and long exposure images using a convolutional neural network for joint denoising and deblurring, resulting in high-quality images that outperform existing methods in challenging conditions.
The paper addresses the problem of acquiring high-quality photographs with handheld smartphone cameras in low-light imaging conditions. We propose an approach based on capturing pairs of short and long exposure images in rapid succession and fusing them into a single high-quality photograph. Unlike existing methods, we take advantage of both images simultaneously and perform a joint denoising and deblurring using a convolutional neural network. A novel approach is introduced to generate realistic short-long exposure image pairs. The method produces good images in extremely challenging conditions and outperforms existing denoising and deblurring methods. It also enables exposure fusion in the presence of motion blur.