Image deblurring is an ill-posed problem with multiple plausible solutions for a given input
image. However, most existing methods produce a deterministic estimate of the clean image …

Images taken in dynamic scenes may contain unwanted motion blur, which significantly
degrades visual quality. Such blur causes short-and long-range region-specific smoothing …

Diffusion models have achieved promising results in image restoration tasks, yet suffer from
time-consuming, excessive computational resource consumption, and unstable restoration …

Image restoration aims to reconstruct a high-quality image from its corrupted version, playing
essential roles in many scenarios. Recent years have witnessed a paradigm shift in image …

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 …

Abstract Diffusion Probabilistic Models (DPMs) have recently been employed for image
deblurring, formulated as an image-conditioned generation process that maps Gaussian …

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 …

Image-based single-modality compression learning approaches have demonstrated
exceptionally powerful encoding and decoding capabilities in the past few years, but suffer …

We present IRNeXt, a simple yet effective convolutional network architecture for image
restoration. Recently, Transformer models have dominated the field of image restoration due …

In this paper, we present the multi-stage attentive network (MSAN), an efficient and good
generalization performance convolutional neural network (CNN) architecture for motion …