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Works189 from public data
- 1,955
- Single Image Dehazing via Multi-scale Convolutional Neural Networks1,809
A multi-scale deep neural network for single-image dehazing by learning the mapping between hazy images and their corresponding transmission maps by combining a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale network which refines results locally.
- Multi-Scale Boosted Dehazing Network With Dense Feature Fusion1,100
This paper designs a dense feature fusion module that can simultaneously remedy the missing spatial information from high-resolution features and exploit the non-adjacent features and develops a simple yet effective boosted decoder to progressively restore the haze-free image.
- 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.
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- Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges387
A multi-scale deep neural network for single image dehazing by learning the mapping between hazy images and their transmission maps by combining a coarse-scale net which predicts a holistic transmission map based on the entire image, and a fine-scale network which refines dehazed results locally.
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- 297
- Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution279
This work proposes a spatially-adaptive feature modulation (SAFM) mechanism for efficient SR design that is 3× smaller than state-of-the-art efficient SR methods, e.g., IMDN, and yields comparable performance with much less memory usage.
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- 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.
- Adapt or Perish: Adaptive Sparse Transformer with Attentive Feature Refinement for Image Restoration226
- 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.
- Unpaired Deep Image Deraining Using Dual Contrastive Learning205
An effective unpaired SID adversarial framework which explores mutual properties of the unpaired exemplars by a dual contrastive learning manner in a deep feature space, named as DCD-GAN is developed.
- 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.
- Physics-Based Feature Dehazing Networks184
An effective feature dehazing unit (FDU), which is applied to the deep feature space to explore useful features for image dehazed based on the physics model, and can effectively dehaze images with favorable performance against state-of-the-art methods.
- Cascaded Deep Video Deblurring Using Temporal Sharpness Prior182
A temporal sharpness prior to constrain the deep CNN model to help the latent frame restoration and it is shown that exploring the domain knowledge of video deblurring is able to make the deepCNN model more compact and efficient.
- Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network169
Experimental results show that many low-level vision tasks can be effectively learned and carried out in real-time by the proposed algorithm and is significantly smaller and faster in comparison with a deep CNN based image filter.
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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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