Images taken under the low-light condition often contain blur and saturated pixels at the
same time. Deblurring images with saturated pixels is quite challenging. Because of the …

Blind image deblurring (BID) has been extensively studied in computer vision and adjacent
fields. Modern methods for BID can be grouped into two categories: single-instance methods …

Recent studies in Radiance Fields have paved the robust way for novel view synthesis with
their photorealistic rendering quality. Nevertheless, they usually employ neural networks …

Motion deblurring is one of the fundamental problems of computer vision and has received
continuous attention. The variability in blur, both within and across images, imposes …

In smartphones and compact cameras, the Image Signal Processor (ISP) transforms the
RAW sensor image into a human-readable sRGB image. Most popular super-resolution …

Neural networks allow solving many ill-posed inverse problems with unprecedented
performance. Physics informed approaches already progressively replace carefully hand …

While diffusion-based image restoration (IR) methods have achieved remarkable success,
they are still limited by the low inference speed attributed to the necessity of executing …

Abstract Neural Radiance Fields (NeRF) has shown its remarkable performance in neural
rendering-based novel view synthesis. However, NeRF suffers from severe visual quality …

Significant progress in image deblurring has been achieved by deep learning methods
especially the remarkable performance of supervised models on paired synthetic data …

This paper proposes\textbf {UHDformer}, a general Transformer for Ultra-High-Definition
(UHD) image restoration. UHDformer contains two learning spaces:\textbf {(a)} learning in …