Multi-modality (MM) image fusion aims to render fused images that maintain the merits of
different modalities, eg, functional highlight and detailed textures. To tackle the challenge in …

Exposure correction task aims to correct the underexposure and its adverse overexposure
images to the normal exposure in a single network. As well recognized, the optimization flow …

The asymmetric dual-lens configuration is commonly available on mobile devices
nowadays, which naturally stores a pair of wide-angle and telephoto images of the same …

Multi-modality image fusion (MMIF) aims to integrate complementary information from
different modalities into a single fused image to represent the imaging scene and facilitate …

Deep learning-based source dehazing methods trained on synthetic datasets have
achieved remarkable performance but suffer from dramatic performance degradation on real …

Photon-efficient imaging has enabled a number of applications relying on single-photon
sensors that can capture a 3D image with as few as one photon per pixel. In practice …

Image fusion aims to generate a high-quality image from multiple images captured under
varying conditions. The key problem of this task is to preserve complementary information …

Image fusion is a significant problem in many fields including digital photography,
computational imaging and remote sensing, to name but a few. Recently, deep learning has …

Stereo image pairs record the scene from two different views and introduce cross-view
information for image restoration. However, there are two challenges in utilizing the cross …

Existing methods for event stream super-resolution (SR) either require high-quality and high-
resolution frames or underperform for large factor SR. To address these problems, we …