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Found 21 papers, 5 papers with code
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
On learning optimized reaction diffusion processes for effective image restoration
We propose to train the parameters of the filters and the influence functions through a loss based approach.
Self-Supervised Learning for Real-World Super-Resolution from Dual Zoomed Observations
For this purpose, we take the telephoto image instead of an additional high-resolution image as the supervision information and select a center patch from it as the reference to super-resolve the corresponding short-focus image patch.
NTIRE 2022 Challenge on Super-Resolution and Quality Enhancement of Compressed Video: Dataset, Methods and Results
This challenge includes three tracks.
DINN360: Deformable Invertible Neural Network for Latitude-Aware 360deg Image Rescaling
To alleviate this issue, we propose the first attempt at 360deg image rescaling, which refers to downscaling a 360deg image to a visually valid low-resolution (LR) counterpart and then upscaling to a high-resolution (HR) 360deg image given the LR variant.
LEARN: Learned Experts' Assessment-based Reconstruction Network for Sparse-data CT
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.
Speckle Reduction with Trained Nonlinear Diffusion Filtering
Therefore, in this study we aim to propose an efficient despeckling model with both high computational efficiency and high recovery quality.
Learning Non-local Image Diffusion for Image Denoising
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 via Multi-scale Nonlinear Diffusion Models
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.
Poisson Noise Reduction with Higher-order Natural Image Prior Model
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.
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
The TNRD approach is applicable for a variety of image restoration tasks by incorporating appropriate reaction force.
Ranked #6 on
Color Image Denoising
on Darmstadt Noise Dataset
Higher-order MRFs based image super resolution: why not MAP?
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.
Fast and Accurate Poisson Denoising with Optimized Nonlinear Diffusion
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.
A higher-order MRF based variational model for multiplicative noise reduction
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.
A bi-level view of inpainting - based image compression
Inpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression.
iPiano: Inertial Proximal Algorithm for Non-Convex Optimization
A rigorous analysis of the algorithm for the proposed class of problems yields global convergence of the function values and the arguments.
Revisiting loss-specific training of filter-based MRFs for image restoration
It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision.
Learning $\ell_1$-based analysis and synthesis sparsity priors using bi-level optimization
We then introduce an approach to learn both analysis operator and synthesis dictionary simultaneously by using a unified framework of bi-level optimization.
Insights into analysis operator learning: From patch-based sparse models to higher-order MRFs
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.
Learning Generic Diffusion Processes for Image Restoration
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.
Learning Dynamic Guidance for Depth Image Enhancement
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.
