We present an effective and efficient method that explores the properties of Transformers in
the frequency domain for high-quality image deblurring. Our method is motivated by the …

Modern image-based deblurring methods usually show degenerate performance in low-light
conditions since the images often contain most of the poorly visible dark regions and a few …

Non-blind image deconvolution (NBID) is about restoring a latent sharp image from a
blurred one, given an associated blur kernel. Most existing deep neural networks for NBID …

In recent years, algorithm unrolling has emerged as a powerful technique for designing
interpretable neural networks based on iterative algorithms. Imaging inverse problems have …

Classic image-restoration algorithms use a variety of priors, either implicitly or explicitly.
Their priors are hand-designed and their corresponding weights are heuristically assigned …

Non-blind deconvolution aims to restore a sharp image from its blurred counterpart given an
obtained kernel. Existing deep neural architectures are often built based on large datasets of …

Blind image deblurring remains challenging due to the ill-posed nature of the traditional
blurring function. Although previous supervised methods have achieved great breakthrough …

In recent years, deep neural network-based restoration methods have achieved state-of-the-
art results in various image deblurring tasks. However, one major drawback of deep learning …

In vision inspection tasks, moiré patterns caused by frequency aliasing can severely
degrade image quality. To prevent moiré patterns, we used images that were intentionally …

Blind image deblurring aims to recover an output latent image and a blur kernel from a given
blurred image. Kernel estimation is a significant step in blind image deblurring and requires …