9.2k citations · 16.2k across the 175 of their papers we have counts for
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Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Marcos V. Conde, Radu Timofte, Yibin Huang +40
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level…
Multi-stage image denoising with the wavelet transform
Chunwei Tian, Menghua Zheng, Wangmeng Zuo +3
Deep convolutional neural networks (CNNs) are used for image denoising via automatically mining accurate structure information. However, most of existing CNNs depend on enlarging d…
A heterogeneous group CNN for image super-resolution
Chunwei Tian, Yanning Zhang, Wangmeng Zuo +3
Convolutional neural networks (CNNs) have obtained remarkable performance via deep architectures. However, these CNNs often achieve poor robustness for image super-resolution (SR)…
Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
Zhilu Zhang, Ruohao Wang, Hongzhi Zhang +2
In this paper, we consider two challenging issues in reference-based super-resolution (RefSR), (i) how to choose a proper reference image, and (ii) how to learn real-world RefSR in…
Local Patch Network with Global Attention for Infrared Small Target Detection
Fang Chen, Chenqiang Gao, Fangcen Liu +4
Infrared small target detection plays an important role in the infrared search and tracking applications. In recent years, deep learning techniques were introduced to this task and…
Learning Scalable -constrained Near-lossless Image Compression via Joint Lossy Image and Residual Compression
Yuanchao Bai, Xianming Liu, Wangmeng Zuo +2
We propose a novel joint lossy image and residual compression framework for learning -constrained near-lossless image compression. Specifically, we obtain a lossy reco…
Progressive Training of Multi-level Wavelet Residual Networks for Image Denoising
Yali Peng, Yue Cao, Shigang Liu +2
Recent years have witnessed the great success of deep convolutional neural networks (CNNs) in image denoising. Albeit deeper network and larger model capacity generally benefit per…
Unpaired Learning of Deep Image Denoising
Xiaohe Wu, Ming Liu, Yue Cao +2
We investigate the task of learning blind image denoising networks from an unpaired set of clean and noisy images. Such problem setting generally is practical and valuable consider…
Plug-and-Play Image Restoration with Deep Denoiser Prior
Kai Zhang, Yawei Li, Wangmeng Zuo +3
Recent works on plug-and-play image restoration have shown that a denoiser can implicitly serve as the image prior for model-based methods to solve many inverse problems. Such a pr…
Lightweight image super-resolution with enhanced CNN
Chunwei Tian, Ruibin Zhuge, Zhihao Wu +4
Deep convolutional neural networks (CNNs) with strong expressive ability have achieved impressive performances on single image super-resolution (SISR). However, their excessive amo…
Designing and Training of A Dual CNN for Image Denoising
Chunwei Tian, Yong Xu, Wangmeng Zuo +3
Deep convolutional neural networks (CNNs) for image denoising have recently attracted increasing research interest. However, plain networks cannot recover fine details for a comple…
Flexible Image Denoising with Multi-layer Conditional Feature Modulation
Jiazhi Du, Xin Qiao, Zifei Yan +2
For flexible non-blind image denoising, existing deep networks usually take both noisy image and noise level map as the input to handle various noise levels with a single model. Ho…
Learning Context-Based Non-local Entropy Modeling for Image Compression
Mu Li, Kai Zhang, Wangmeng Zuo +2
The entropy of the codes usually serves as the rate loss in the recent learned lossy image compression methods. Precise estimation of the probabilistic distribution of the codes pl…
Deep Adaptive Inference Networks for Single Image Super-Resolution
Ming Liu, Zhilu Zhang, Liya Hou +2
Recent years have witnessed tremendous progress in single image super-resolution (SISR) owing to the deployment of deep convolutional neural networks (CNNs). For most existing meth…
Deep Learning on Image Denoising: An overview
Chunwei Tian, Lunke Fei, Wenxian Zheng +3
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 deali…
Efficient and Effective Context-Based Convolutional Entropy Modeling for Image Compression
Mu Li, Kede Ma, Jane You +2
Precise estimation of the probabilistic structure of natural images plays an essential role in image compression. Despite the recent remarkable success of end-to-end optimized imag…