Search Results for author:
Found 19 papers, 7 papers with code
OID: Outlier Identifying and Discarding in Blind Image Deblurring
Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process.
Enhanced Sparse Model for Blind Deblurring
Specifically, we use a weighted combination of a dense function (i. e. l2) and a newly designed enhanced sparse model termed as le, which is developed from two sparse models (i. e. l1 and l0), to fulfill the task.
Three-Stage Cascade Framework for Blurry Video Frame Interpolation
Besides, experiments on real-world blurry videos also indicate the good generalization ability of our model.
Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction
We test our MC-CDic model on multi-contrast MRI SR and reconstruction tasks.
Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring
In this paper, we develop a data-driven approach to model the saturated pixels by a learned latent map.
Indoor Depth Recovery Based on Deep Unfolding with Non-Local Prior
Utilizing the property that there is a large amount of non-local common characteristics in depth images, we propose a novel model-guided depth recovery method, namely the DC-NLAR model.
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and Reconstruction
We thus build a model to reconstruct the target image and decompose the reference image as a common component and a unique component.
Flow Guidance Deformable Compensation Network for Video Frame Interpolation
Under the guidance of the flow priors learned in step one, the deformation step designs a pyramid deformable compensation network to compensate for the missing details of the flow step.
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning.
Contrastive Learning for Local and Global Learning MRI Reconstruction
Therefore, we propose a Spatial and Fourier Layer (SFL) to simultaneously learn the local and global information in Spatial and Fourier domains.
Structure-Preserving Deraining with Residue Channel Prior Guidance
Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures.
Blind Deblurring for Saturated Images
To address this problem, we introduce a new blur model to fit both saturated and unsaturated pixels, and all informative pixels can be considered during deblurring process.
Learning a Non-Blind Deblurring Network for Night Blurry Images
Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels.
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
Besides the cross-view information exploitation in the low-resolution (LR) space, HR representations produced by the SR process are utilized to perform HR disparity estimation with higher accuracy, through which the HR features can be aggregated to generate a finer SR result.
Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing
To achieve this, we propose a Multi-scale Topological Network (MSTN) to fully explore the features at different scales.
A Novel Retinex-Based Fractional-Order Variational Model for Image with Severely Low Light
The proposed method is more flexible in controlling the reg- ularization extent than the existing integer-order regularization methods.
MDCN: Multi-scale Dense Cross Network for Image Super-Resolution
Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model.
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging.
Multi-scale Residual Network for Image Super-Resolution
Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB).
