Search Results for author: Yunjin Chen

Found 20 papers, 4 papers with code

Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations

no code implementations2 Mar 2022 Zhilu Zhang, Ruohao Wang, Hongzhi Zhang, Yunjin Chen, WangMeng Zuo

For the first issue, the more zoomed (telephoto) image can be naturally leveraged as the reference to guide the SR of the lesser zoomed (short-focus) image.

Reference-based Super-Resolution Self-Supervised Learning

Learning Generic Diffusion Processes for Image Restoration

no code implementations17 Jul 2018 Peng Qiao, Yong Dou, Yunjin Chen, Wensen Feng

On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained.

Denoising Image Restoration

LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT

no code implementations30 Jul 2017 Hu Chen, Yi Zhang, Yunjin Chen, Junfeng Zhang, Weihua Zhang, Huaiqiaing Sun, Yang Lv, Peixi Liao, Jiliu Zhou, Ge Wang

Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on.

Compressive Sensing

Learning Dynamic Guidance for Depth Image Enhancement

no code implementations CVPR 2017 Shuhang Gu, WangMeng Zuo, Shi Guo, Yunjin Chen, Chongyu Chen, Lei Zhang

To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance.

Depth Image Upsampling Image Enhancement +1

Speckle Reduction with Trained Nonlinear Diffusion Filtering

no code implementations24 Feb 2017 Wensen Feng, Yunjin Chen

Therefore, in this study we aim to propose an efficient despeckling model with both high computational efficiency and high recovery quality.

Image Restoration

Learning Non-local Image Diffusion for Image Denoising

no code implementations24 Feb 2017 Peng Qiao, Yong Dou, Wensen Feng, Yunjin Chen

In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising.

Image Denoising SSIM

Image Denoising via Multi-scale Nonlinear Diffusion Models

no code implementations21 Sep 2016 Wensen Feng, Peng Qiao, Xuanyang Xi, Yunjin Chen

However, in recent two years, discriminatively trained local approaches have started to outperform previous non-local models and have been attracting increasing attentions due to the additional advantage of computational efficiency.

Image Denoising

Poisson Noise Reduction with Higher-order Natural Image Prior Model

no code implementations19 Sep 2016 Wensen Feng, Hong Qiao, Yunjin Chen

We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR.

Denoising Image Restoration

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

15 code implementations13 Aug 2016 Kai Zhang, WangMeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang

Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.

Image Deblocking Image Denoising +3

Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion

no code implementations10 Oct 2015 Wensen Feng, Yunjin Chen

The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision and microscopy.

Denoising Image Restoration

Higher-order MRFs based image super resolution: why not MAP?

no code implementations27 Oct 2014 Yunjin Chen

A trainable filter-based higher-order Markov Random Fields (MRFs) model - the so called Fields of Experts (FoE), has proved a highly effective image prior model for many classic image restoration problems.

Image Restoration Image Super-Resolution +1

A higher-order MRF based variational model for multiplicative noise reduction

no code implementations21 Apr 2014 Yunjin Chen, Wensen Feng, René Ranftl, Hong Qiao, Thomas Pock

The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems.

Image Restoration

iPiano: Inertial Proximal Algorithm for Non-Convex Optimization

no code implementations18 Apr 2014 Peter Ochs, Yunjin Chen, Thomas Brox, Thomas Pock

A rigorous analysis of the algorithm for the proposed class of problems yields global convergence of the function values and the arguments.

Computer Vision Image Compression +1

A bi-level view of inpainting - based image compression

no code implementations16 Jan 2014 Yunjin Chen, René Ranftl, Thomas Pock

Inpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression.

Image Compression

Revisiting loss-specific training of filter-based MRFs for image restoration

no code implementations16 Jan 2014 Yunjin Chen, Thomas Pock, René Ranftl, Horst Bischof

It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision.

Image Denoising Image Restoration

Learning $\ell_1$-based analysis and synthesis sparsity priors using bi-level optimization

no code implementations16 Jan 2014 Yunjin Chen, Thomas Pock, Horst Bischof

We then introduce an approach to learn both analysis operator and synthesis dictionary simultaneously by using a unified framework of bi-level optimization.

Dictionary Learning Image Denoising

Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs

no code implementations13 Jan 2014 Yunjin Chen, René Ranftl, Thomas Pock

Numerical experiments show that our trained models clearly outperform existing analysis operator learning approaches and are on par with state-of-the-art image denoising algorithms.

Image Denoising Image Restoration +1

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