NTIRE 2021 challenge on image deblurring
Motion blur is a common photography artifact in dynamic environments that typically comes
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …
jointly with the other types of degradation. This paper reviews the NTIRE 2021 Challenge on …
Efficient and explicit modelling of image hierarchies for image restoration
The aim of this paper is to propose a mechanism to efficiently and explicitly model image
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …
hierarchies in the global, regional, and local range for image restoration. To achieve that, we …
Deblurring via stochastic refinement
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 …
image. However, most existing methods produce a deterministic estimate of the clean image …
Efficient frequency domain-based transformers for high-quality image deblurring
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 …
the frequency domain for high-quality image deblurring. Our method is motivated by the …
Promptir: Prompting for all-in-one image restoration
Image restoration involves recovering a high-quality clean image from its degraded version.
Deep learning-based methods have significantly improved image restoration performance …
Deep learning-based methods have significantly improved image restoration performance …
Event-based fusion for motion deblurring with cross-modal attention
Traditional frame-based cameras inevitably suffer from motion blur due to long exposure
times. As a kind of bio-inspired camera, the event camera records the intensity changes in …
times. As a kind of bio-inspired camera, the event camera records the intensity changes in …
Hierarchical integration diffusion model for realistic image deblurring
Diffusion models (DMs) have recently been introduced in image deblurring and exhibited
promising performance, particularly in terms of details reconstruction. However, the diffusion …
promising performance, particularly in terms of details reconstruction. However, the diffusion …
Deep learning in motion deblurring: current status, benchmarks and future prospects
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 …
continuous attention. The variability in blur, both within and across images, imposes …
FCL-GAN: A lightweight and real-time baseline for unsupervised blind image deblurring
Blind image deblurring (BID) remains a challenging and significant task. Benefiting from the
strong fitting ability of deep learning, paired data-driven supervised BID methods have …
strong fitting ability of deep learning, paired data-driven supervised BID methods have …
Efficient multi-scale network with learnable discrete wavelet transform for blind motion deblurring
Coarse-to-fine schemes are widely used in traditional single-image motion deblur; however
in the context of deep learning existing multi-scale algorithms not only require the use of …
in the context of deep learning existing multi-scale algorithms not only require the use of …