29 citations · 65 across the 13 of their papers we have counts for
13 papers
Fast, nonlocal and neural: a lightweight high quality solution to image denoising
Yu Guo, Axel Davy, Gabriele Facciolo +2
With the widespread application of convolutional neural networks (CNNs), the traditional model based denoising algorithms are now outperformed. However, CNNs face two problems. Fir…
Synthesizing PET images from High-field and Ultra-high-field MR images Using Joint Diffusion Attention Model
Taofeng Xie, Chentao Cao, Zhuoxu Cui +14
MRI and PET are crucial diagnostic tools for brain diseases, as they provide complementary information on brain structure and function. However, PET scanning is costly and involves…
Image Denoising by Gaussian Patch Mixture Model and Low Rank Patches
Jing Guo, Shuping Wang, Chen Luo +2
Non-local self-similarity based low rank algorithms are the state-of-the-art methods for image denoising. In this paper, a new method is proposed by solving two issues: how to impr…
Edge Adaptive Hybrid Regularization Model For Image Deblurring
Tingting Zhang, Jie Chen, Caiying Wu +3
The parameter selection is crucial to regularization based image restoration methods. Generally speaking, a spatially fixed parameter for regularization item in the whole image doe…
Non-Local Robust Quaternion Matrix Completion for Color Images and Videos Inpainting
Zhigang Jia, Qiyu Jin, Michael K. Ng +1
The image nonlocal self-similarity (NSS) prior refers to the fact that a local patch often has many nonlocal similar patches to it across the image and has been widely applied in m…
Joint Demosaicking and Denoising Benefits from a Two-stage Training Strategy
Yu Guo, Qiyu Jin, Gabriele Facciolo +2
Image demosaicking and denoising are the first two key steps of the color image production pipeline. The classical processing sequence has for a long time consisted of applying den…
A Review of an Old Dilemma: Demosaicking First, or Denoising First?
Qiyu Jin, Gabriele Facciolo, Jean-Michel Morel
Image denoising and demosaicking are the most important early stages in digital camera pipelines. They constitute a severely ill-posed problem that aims at reconstructing a full co…
A Non-Local Means Filter for Removing the Poisson Noise
Qiyu Jin, Ion Grama, Quansheng Liu
A new image denoising algorithm to deal with the Poisson noise model is given, which is based on the idea of Non-Local Mean. By using the "Oracle" concept, we establish a theorem t…
Convergence Theorems for the Non-Local Means Filter
Qiyu Jin, Ion Grama, Quansheng Liu
In this paper, we establish convergence theorems for the Non-Local Means Filter in removing the additive Gaussian noise. We employ the techniques of "Oracle" estimation to determin…
Optimal Weights Mixed Filter for Removing Mixture of Gaussian and Impulse Noises
Qiyu Jin, Ion Grama, Quansheng Liu
According to the character of Gaussian, we modify the Rank-Ordered Absolute Differences (ROAD) to Rank-Ordered Absolute Differences of mixture of Gaussian and impulse noises (ROADG…
A New Poisson Noise Filter based on Weights Optimization
Qiyu Jin, Ion Grama, Quansheng Liu
We propose a new image denoising algorithm when the data is contaminated by a Poisson noise. As in the Non-Local Means filter, the proposed algorithm is based on a weighted linear…
Controlled Total Variation regularization for inverse problems
Qiyu Jin, Ion Grama, Quansheng Liu
This paper provides a new algorithm for solving inverse problems, based on the minimization of the norm and on the control of the Total Variation. It consists in relaxing the…
Removing Gaussian Noise by Optimization of Weights in Non-Local Means
Qiyu Jin, Ion Grama, Quansheng Liu
A new image denoising algorithm to deal with the additive Gaussian white noise model is given. Like the non-local means method, the filter is based on the weighted average of the o…