Low-light photography presents significant challenges. Multi-image processing methods
have made numerous attempts to obtain high-quality photos yet remain unsatisfactory …

Employing specific networks to address different types of degradation often proved to be
complex and time-consuming in practical applications. The Bracket Image Restoration and …

Due to the difficulty of collecting real paired data, most existing desmoking methods train the
models by synthesizing smoke, generalizing poorly to real surgical scenarios. Although a …

Burst super-resolution (BurstSR) utilizes signal information from multiple adjacent frames
successively taken to restore rich textures. However, due to hand tremors and other image …

Burst image restoration methods offer the possibility of recovering faithful scene details from
multiple low-quality snapshots captured by hand-held devices in adverse scenarios, thereby …

Burst super-resolution aims to reconstruct high-resolution images with higher quality and
richer details by fusing the sub-pixel information from multiple burst low-resolution frames. In …

Despite the significant progress in image denoising, it is still challenging to restore fine-scale
details while removing noise, especially in extremely low-light environments. Leveraging …

Multi-frame super-resolution (MFSR) leverages complementary information between image
sequences of the same scene to increase the resolution of the reconstructed image. As a …