Deep learning on image denoising: An overview
Deep learning techniques have received much attention in the area of image denoising.
However, there are substantial differences in the various types of deep learning methods …
However, there are substantial differences in the various types of deep learning methods …
NTIRE 2023 challenge on image denoising: Methods and results
This paper reviews the NTIRE 2023 challenge on image denoising (sigma= 50) with a focus
on the proposed solutions and results. The aim is to obtain a network design capable to …
on the proposed solutions and results. The aim is to obtain a network design capable to …
Maxim: Multi-axis mlp for image processing
Recent progress on Transformers and multi-layer perceptron (MLP) models provide new
network architectural designs for computer vision tasks. Although these models proved to be …
network architectural designs for computer vision tasks. Although these models proved to be …
Restormer: Efficient transformer for high-resolution image restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image
priors from large-scale data, these models have been extensively applied to image …
priors from large-scale data, these models have been extensively applied to image …
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 …
Learning enriched features for fast image restoration and enhancement
Given a degraded input image, image restoration aims to recover the missing high-quality
image content. Numerous applications demand effective image restoration, eg …
image content. Numerous applications demand effective image restoration, eg …
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 …
Hinet: Half instance normalization network for image restoration
In this paper, we explore the role of Instance Normalization in low-level vision tasks.
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to …
Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to …
Multi-stage progressive image restoration
Image restoration tasks demand a complex balance between spatial details and high-level
contextualized information while recovering images. In this paper, we propose a novel …
contextualized information while recovering images. In this paper, we propose a novel …
Data collection and quality challenges in deep learning: A data-centric ai perspective
Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …
learning becomes the new software, powered by big data and computing infrastructure …