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Found 34 papers, 6 papers with code
Bayesian Nonparametric Dictionary Learning for Compressed Sensing MRI
The size of the dictionary and the patch-specific sparsity pattern are inferred from the data, in addition to other dictionary learning variables.
A Weighted Variational Model for Simultaneous Reflectance and Illumination Estimation
We propose a weighted variational model to estimate both the reflectance and the illumination from an observed image.
Clearing the Skies: A deep network architecture for single-image rain removal
We introduce a deep network architecture called DerainNet for removing rain streaks from an image.
Ranked #11 on
Single Image Deraining
on Test100
(SSIM metric)
Removing Rain From Single Images via a Deep Detail Network
We propose a new deep network architecture for removing rain streaks from individual images based on the deep convolutional neural network (CNN).
PanNet: A Deep Network Architecture for Pan-Sharpening
We incorporate domain-specific knowledge to design our PanNet architecture by focusing on the two aims of the pan-sharpening problem: spectral and spatial preservation.
Residual-Guide Feature Fusion Network for Single Image Deraining
Single image rain streaks removal is extremely important since rainy images adversely affect many computer vision systems.
Lightweight Pyramid Networks for Image Deraining
In this paper, we propose a lightweight pyramid of networks (LPNet) for single image deraining.
A Deep Tree-Structured Fusion Model for Single Image Deraining
We propose a simple yet effective deep tree-structured fusion model based on feature aggregation for the deraining problem.
A^2Net: Adjacent Aggregation Networks for Image Raindrop Removal
Existing methods for single images raindrop removal either have poor robustness or suffer from parameter burdens.
A Variational Pan-Sharpening With Local Gradient Constraints
Then a more accurate spatial preservation based on local gradient constraints is incorporated into the objective to fully utilize spatial information contained in the PAN image.
JPEG Artifacts Reduction via Deep Convolutional Sparse Coding
To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse coding (DCSC) network architecture.
Noise2Blur: Online Noise Extraction and Denoising
Using the new image pair, the denoising network learns to generate clean and high-quality images from noisy observations.
Real-world Person Re-Identification via Degradation Invariance Learning
Person re-identification (Re-ID) in real-world scenarios usually suffers from various degradation factors, e. g., low-resolution, weak illumination, blurring and adverse weather.
Learning Dual Priors for JPEG Compression Artifacts Removal
Specifically, we design a variational model to formulate the image de-blocking problem and propose two prior terms for the image content and gradient, respectively.
Cross-Patch Graph Convolutional Network for Image Denoising
However, the real noisy images in practical are mostly of high resolution rather than the cropped small patches and the vanilla training strategies ignore the cross-patch contextual dependency in the whole image.
Attack-Guided Perceptual Data Generation for Real-World Re-Identification
In unconstrained real-world surveillance scenarios, person re-identification (Re-ID) models usually suffer from different low-level perceptual variations, e. g., cross-resolution and insufficient lighting.
Improving De-Raining Generalization via Neural Reorganization
Equipped with our NR algorithm, the deep model can be trained on a list of synthetic rainy datasets by overcoming catastrophic forgetting, making it a general-version de-raining network.
Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement
Our approach, termed Twice Mixing, is motivated by the observation that a mid-quality image can be generated by mixing a high-quality image with its low-quality version.
Image De-Raining via Continual Learning
The proposed model is capable of achieving superior performance on both inhomogeneous and incremental datasets, and is promising for highly compact systems to gradually learn myriad regularities of the different types of rain streaks.
Space-Time Distillation for Video Super-Resolution
In this paper, we aim to improve the performance of compact VSR networks without changing their original architectures, through a knowledge distillation approach that transfers knowledge from a complicated VSR network to a compact one.
Unfolding Taylor's Approximations for Image Restoration
Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image.
Exposure Normalization and Compensation for Multiple-Exposure Correction
However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple exposures, thus resulting in poor performance.
Dreaming To Prune Image Deraining Networks
It is based on our observation that deep degradation representations can be clustered by degradation characteristics (types of rain) while independent of image content.
Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening
Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success.
Bijective Mapping Network for Shadow Removal
Shadow removal, which aims to restore the background in the shadow regions, is challenging due to the highly ill-posed nature.
Mutual Information-Driven Pan-Sharpening
Despite the remarkable progress, existing state-of-the-art Pan-sharpening methods don't explicitly enforce the complementary information learning between two modalities of PAN and MS images.
Efficient Model-Driven Network for Shadow Removal
To address these issues, we first propose a new shadow illumination model for the shadow removal task.
Event-Guided Person Re-Identification via Sparse-Dense Complementary Learning
Video-based person re-identification (Re-ID) is a prominent computer vision topic due to its wide range of video surveillance applications.
Representation Learning
Video-Based Person Re-Identification
Generalized UAV Object Detection via Frequency Domain Disentanglement
This loss enables the network to concentrate on extracting domain-invariant spectrum and domain-specific spectrum, so as to achieve better disentangling results.
Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions
Inspired by this observation, we design an efficient unified framework with a two-stage training strategy to explore the weather-general and weather-specific features.
Edge-Aware Regional Message Passing Controller for Image Forgery Localization
In the second step, guided by the learnable edges, a region message passing controller is devised to weaken the message passing between the forged and authentic regions.
Revisiting Single Image Reflection Removal In the Wild
This research focuses on the issue of single-image reflection removal (SIRR) in real-world conditions, examining it from two angles: the collection pipeline of real reflection pairs and the perception of real reflection locations.
Decoupling Degradation and Content Processing for Adverse Weather Image Restoration
The former facilitates channel-dependent degradation removal operation, allowing the network to tailor responses to various adverse weather types; the latter, by integrating Fourier's global properties into channel-independent content features, enhances network capacity for consistent global content reconstruction.
CCM: Adding Conditional Controls to Text-to-Image Consistency Models
Consistency Models (CMs) have showed a promise in creating visual content efficiently and with high quality.
