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- Gated Fusion Network for Single Image Dehazing953
An efficient algorithm to directly restore a clear image from a hazy input using an end-to-end trainable neural network that consists of an encoder and a decoder is proposed.
- Unsupervised Image Super-Resolution Using Cycle-in-Cycle Generative Adversarial Networks619
This work proposes a Cycle-in-Cycle network structure with generative adversarial networks (GAN) as the basic component to tackle the single image super-resolution problem in a more general case that the low-/high-resolution pairs and the down-sampling process are unavailable.
- CREST: Convolutional Residual Learning for Visual Tracking553
This paper proposes the CREST algorithm to reformulate DCFs as a one-layer convolutional neural network, and applies residual learning to take appearance changes into account to reduce model degradation during online update.
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- DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization383
DNGaussian is introduced, a depth-regularized framework based on 3D Gaussian radiance fields, offering real-time and high-quality few-shot novel view synthesis at low costs, and a Hard and Soft Depth Regularization to restore accurate scene geometry under coarse monocular depth supervision while maintaining a fine-grained color appearance.
- Spatio-Temporal Filter Adaptive Network for Video Deblurring237
The proposed Spatio-Temporal Filter Adaptive Network (STFAN) takes both blurry and restored images of the previous frame as well as blurry image of the current frame as input, and dynamically generates the spatially adaptive filters for the alignment and deblurring.
- Learning Dual Convolutional Neural Networks for Low-Level Vision221
The proposed DualCNN consists of two parallel branches, which respectively recovers the structures and details in an end-to-end manner, and can generate the target signals according to the formation model for each particular application.
- Learning Fully Convolutional Networks for Iterative Non-blind Deconvolution200
The proposed fully convolutional network is able to learn an adaptive image prior, which keeps both local and global information, and performs favorably against state-of-the-art algorithms in terms of quality and speed.
- Physics-Based Generative Adversarial Models for Image Restoration and Beyond188
An algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, and image deraining) by generative models with adversarial learning within the GAN framework.
- RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs177
This work proposes a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators and outperforms advanced state-of-the-art methods on one synthetic dataset and three real-world datasets.
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- DAVANet: Stereo Deblurring With View Aggregation108
This work proposes a novel stereo image deblurring network with Depth Awareness and View Aggregation, named DAVANet, which outperforms state-of-the-art methods in terms of accuracy, speed, and model size.
- Learning to Hallucinate Face Images via Component Generation and Enhancement107
Facial components are generated to approximate ground truth global appearance in the first stage and enhance them through recovering details in the second stage to perform favorably against state-of-the-art methods.
- Grassmann Pooling as Compact Homogeneous Bilinear Pooling for Fine-Grained Visual Classification102
An alternative pooling method which transforms the CNN feature matrix to an orthonormal matrix consists of its principal singular vectors, which enables a very compact feature and classifier representation on a variety of fine-grained image classification datasets.
- Deep Blind Video Super-resolution98
An effective blind video SR algorithm based on deep convolutional neural networks (CNNs) and an effective sharp feature exploration method which first extracts sharp features from restored intermediate latent frames and then uses a transformation operation based on the extracted sharp features and warped features from LR inputs to generate better features for HR video restoration.
- Deep Non-Blind Deconvolution via Generalized Low-Rank Approximation96
The authors' analysis shows that the estimated decomposed matrices contain the most essential information of the input kernel, which ensures the proposed network to handle various blurs in a unified framework and generate high-quality deblurring results.
- AIM 2020 Challenge on Learned Image Signal Processing Pipeline90
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results, defining the state-of-the-art for practical image signal processing pipeline modeling.
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- EfficientFCN: Holistically-Guided Decoding for Semantic Segmentation61
The EfficientFCN is proposed, whose backbone is a common ImageNet pre-trained network without any dilated convolution, and achieves comparable or even better performance than state-of-the-art methods with only 1/3 of the computational cost.
- Joint Face Hallucination and Deblurring via Structure Generation and Detail Enhancement60
This paper proposes a facial component guided deep Convolutional Neural Network to restore a coarse face image, which is denoted as the base image where the facial component is automatically generated from the input face image.
- Dual Convolutional Neural Networks for Low-Level Vision59
Experimental results show that the DualCNN can be effectively applied to numerous low-level vision tasks with favorable performance against the state-of-the-art methods that have been specially designed for each individual task.
- RestoreFormer++: Towards Real-World Blind Face Restoration From Undegraded Key-Value Pairs56
This work proposes RestoreFormer++, which on the one hand introduces fully-spatial attention mechanisms to model the contextual information and the interplay with the priors, and on the other hand explores an extending degrading model to help generate more realistic degraded face images to alleviate the synthetic-to-real-world gap.
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- Diffusion-based Blind Text Image Super-Resolution43
Extensive experiments on synthetic and real-world datasets demonstrate that the Diffusion-based Blind Text Image Super-Resolution (DiffTSR) can restore text images with more accurate text structures as well as more realistic appearances simultaneously.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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