The advent of deep learning has brought a revolutionary transformation to image denoising
techniques. However, the persistent challenge of acquiring noise-clean pairs for supervised …

Abstract NeRF (Neural Radiance Fields) has demonstrated tremendous potential in novel
view synthesis and 3D reconstruction, but its performance is sensitive to input image quality …

In recent years, self-supervised denoising methods have gained significant success and
become critically important in the field of image restoration. Among them, the blind spot …

Image restoration techniques, spanning from the convolution to the transformer paradigm,
have demonstrated robust spatial representation capabilities to deliver high-quality …

Self-supervised denoising has attracted widespread attention due to its ability to train
without clean images. However noise in real-world scenarios is often spatially correlated …

Novel View Synthesis plays a crucial role by generating new 2D renderings from multi-view
images of 3D scenes. However, capturing high-speed scenes with conventional cameras …

We introduce a novel approach to single image denoising based on the Blind Spot
Denoising principle which we call MAsked and SHuffled Blind Spot Denoising (MASH). We …

Burst super-resolution (BurstSR) aims at reconstructing a high-resolution (HR) image from a
sequence of low-resolution (LR) and noisy images, which is conducive to enhancing the …

RGBW color filter arrays (CFAs) have gained widespread attention for their superior
performance in low-light conditions. Most existing demosaicking methods are tailored for …

Blind-spot networks (BSN) have been prevalent network architectures in self-supervised
image denoising (SSID). Existing BSNs are mostly conducted with convolution layers …