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Claim this profileWorks129 from public data
- 2,300
- Learning Deep CNN Denoiser Prior for Image Restoration2,156
Experimental results demonstrate that the learned set of denoisers can not only achieve promising Gaussian denoising results but also can be used as prior to deliver good performance for various low-level vision applications.
- 1,259
- Weighted Nuclear Norm Minimization and Its Applications to Low Level Vision842
It is proved that WNNP is equivalent to a standard quadratic programming problem with linear constrains, which facilitates solving the original problem with off-the-shelf convex optimization solvers and presents an automatic weight setting method, which greatly facilitates the practical implementation of WNNM.
- Learning Convolutional Networks for Content-Weighted Image Compression421
The bit rate of the different parts of the image is adapted to local content, and the content-aware bit rate is allocated under the guidance of a content-weighted importance map so that the sum of the importance map can serve as a continuous alternative of discrete entropy estimation to control compression rate.
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- Projective dictionary pair learning for pattern classification328
Compared with conventional DL methods, the proposed DPL method can not only greatly reduce the time complexity in the training and testing phases, but also lead to very competitive accuracies in a variety of visual classification tasks.
- You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network296
A novel method, called you only look yourself (YOLY), which could be one of the first unsupervised and untrained neural networks for image dehazing which bypasses the conventional training paradigm of deep models on hazy-clean pairs or a large scale dataset, thus avoids the labor-intensive data collection and the domain shift issue.
- Frequency Separation for Real-World Super-Resolution282
DSGAN is proposed to introduce natural image characteristics in bicubic downscaled images to improve the performance of the SR model and separate the low and high image frequencies and treat them differently during training.
- Video Super-Resolution with Recurrent Structure-Detail Network269
A novel recurrent video super-resolution method which is both effective and efficient in exploiting previous frames to super-resolve the current frame by dividing the input into structure and detail components which are fed to a recurrent unit composed of several proposed two-stream structure-detail blocks.
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- 245
- Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression220
This paper analyzes two popular network compression techniques, i.e. filter pruning and low-rank decomposition, in a unified sense and proposes to compress the whole network jointly instead of in a layer-wise manner.
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- Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training173
This paper proposes a novel domain-distance aware super-resolution (DASR) approach for unsupervised real-world image SR and results show that DASR consistently outperforms state-of-the-art un supervised SR approaches in generating SR outputs with more realistic and natural textures.
- 173
- Efficient Video Super-Resolution through Recurrent Latent Space Propagation173
An efficient recurrent latent space propagation algorithm for fast VSR that introduces high-dimensional latent states to propagate temporal information between frames in an implicit manner and outperform current state-of-the-art method DUF with over 70x speed-up.
- Flow-based Kernel Prior with Application to Blind Super-Resolution164
This paper proposes a normalizing flow-based kernel prior (FKP) that optimizes the kernel in the latent space rather than the network parameter space, which allows it to generate reasonable kernel initialization, traverse the learned kernel manifold and improve the optimization stability.
- PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report161
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones and proposes solutions that significantly improved baseline results defining the state-of-the-art for image enhancement on smartphones.
- Improving Deep Video Compression by Resolution-Adaptive Flow Coding149
A new framework called Resolution-adaptive Flow Coding (RaFC) is proposed to effectively compress the flow maps globally and locally, in which multi-resolution representations are used for both the input flow maps and the output motion features of the MV encoder.
- DHP: Differentiable Meta Pruning via HyperNetworks140
This paper introduces a differentiable pruning method via hypernetworks for automatic network pruning, and extensive experiments are conducted on various networks for image classification, single image super-resolution, and denoising.
- Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary136
A group of auxiliary Adaptive Token Dictionary to SR Transformer is introduced and an ATD-SR method is established to establish an adaptive refinement step that achieves the best performance on various single image super-resolution benchmarks.
- AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and Results123
The AIM 2019 challenge on real world super-resolution addresses the real world setting, where paired true high and low-resolution images are unavailable, and aims to advance the state-of-the-art and provide a standard benchmark for this newly emerging task.
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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