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Simple baselines for image restoration
Although there have been significant advances in the field of image restoration recently, the
system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may …
system complexity of the state-of-the-art (SOTA) methods is increasing as well, which may …
A comprehensive review of deep learning-based real-world image restoration
Real-world imagery does not always exhibit good visibility and clean content, but often
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …
Deep generalized unfolding networks for image restoration
Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …
most DNN methods are designed as a black box, lacking transparency and interpretability …
Uformer: A general u-shaped transformer for image restoration
In this paper, we present Uformer, an effective and efficient Transformer-based architecture
for image restoration, in which we build a hierarchical encoder-decoder network using the …
for image restoration, in which we build a hierarchical encoder-decoder network using the …
Masked image training for generalizable deep image denoising
When capturing and storing images, devices inevitably introduce noise. Reducing this noise
is a critical task called image denoising. Deep learning has become the de facto method for …
is a critical task called image denoising. Deep learning has become the de facto method for …
A cross transformer for image denoising
Deep convolutional neural networks (CNNs) depend on feedforward and feedback ways to
obtain good performance in image denoising. However, how to obtain effective structural …
obtain good performance in image denoising. However, how to obtain effective structural …
Refusion: Enabling large-size realistic image restoration with latent-space diffusion models
This work aims to improve the applicability of diffusion models in realistic image restoration.
Specifically, we enhance the diffusion model in several aspects such as network …
Specifically, we enhance the diffusion model in several aspects such as network …
Spectral enhanced rectangle transformer for hyperspectral image denoising
Denoising is a crucial step for hyperspectral image (HSI) applications. Though witnessing
the great power of deep learning, existing HSI denoising methods suffer from limitations in …
the great power of deep learning, existing HSI denoising methods suffer from limitations in …
Ap-bsn: Self-supervised denoising for real-world images via asymmetric pd and blind-spot network
Blind-spot network (BSN) and its variants have made significant advances in self-supervised
denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less practical …
denoising. Nevertheless, they are still bound to synthetic noisy inputs due to less practical …
Intriguing findings of frequency selection for image deblurring
Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image
and the blur kernel given a blurry image. Recent progress on image deblurring always …
and the blur kernel given a blurry image. Recent progress on image deblurring always …