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Found 23 papers, 12 papers with code
SGCNeRF: Few-Shot Neural Rendering via Sparse Geometric Consistency Guidance
Neural Radiance Field (NeRF) technology has made significant strides in creating novel viewpoints.
Exploiting Self-Supervised Constraints in Image Super-Resolution
Recent advances in self-supervised learning, predominantly studied in high-level visual tasks, have been explored in low-level image processing.
Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification
It involves instance sampling, feature representation, and decision-making.
OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition
Depression Recognition (DR) poses a considerable challenge, especially in the context of the growing concerns surrounding privacy.
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios
Monocular depth estimation from RGB images plays a pivotal role in 3D vision.
DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement
To solve this issue, previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features, limiting the generalization and adaptability of the model.
Dynamic Association Learning of Self-Attention and Convolution in Image Restoration
Thus, we propose to refine background textures with the predicted degradation prior in an association learning manner.
EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
Recently, convolutional networks have achieved remarkable development in remote sensing image Super-Resoltuion (SR) by minimizing the regression objectives, e. g., MSE loss.
Learning from History: Task-agnostic Model Contrastive Learning for Image Restoration
Our approach, named Model Contrastive Learning for Image Restoration (MCLIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks.
Ranked #10 on
Image Super-Resolution
on Manga109 - 4x upscaling
From Generation to Suppression: Towards Effective Irregular Glow Removal for Nighttime Visibility Enhancement
Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images.
Fully $1\times1$ Convolutional Network for Lightweight Image Super-Resolution
By incorporating a parameter-free spatial-shift operation, it equips the fully $1\times1$ convolutional network with powerful representation capability while impressive computational efficiency.
Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
To explore the global dependency in the entire frame sequence, a Long-term Temporal Difference Module (L-TDM) is proposed, where the differences between forward and backward segments are incorporated and activated to guide the modulation of the temporal feature, leading to a holistic global compensation.
Refined Semantic Enhancement towards Frequency Diffusion for Video Captioning
In this paper, we introduce a novel Refined Semantic enhancement method towards Frequency Diffusion (RSFD), a captioning model that constantly perceives the linguistic representation of the infrequent tokens.
Magic ELF: Image Deraining Meets Association Learning and Transformer
Convolutional neural network (CNN) and Transformer have achieved great success in multimedia applications.
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution
Spatial-Temporal Video Super-Resolution (ST-VSR) technology generates high-quality videos with higher resolution and higher frame rates.
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning
Specifically, we propose to exploit the mutual information among them via iterative up-and-down projections, where the spatial and temporal features are fully fused and distilled, helping the high-quality video reconstruction.
Unpaired Deep Image Deraining Using Dual Contrastive Learning
Learning single image deraining (SID) networks from an unpaired set of clean and rainy images is practical and valuable as acquiring paired real-world data is almost infeasible.
From Less to More: Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution
By this spectral splitting and aggregation strategy (SSAS), we can divide the original hyperspectral image into multiple samples (\emph{from less to more}) to support the efficient training of the network and effectively exploit the spectral correlations among spectrum.
Omniscient Video Super-Resolution
Most recent video super-resolution (SR) methods either adopt an iterative manner to deal with low-resolution (LR) frames from a temporally sliding window, or leverage the previously estimated SR output to help reconstruct the current frame recurrently.
Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement
Different from existing methods tending to accomplish the relighting task directly by ignoring the fidelity and naturalness recovery, we investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps.
When Face Recognition Meets Occlusion: A New Benchmark
In particular, we first collect a variety of glasses and masks as occlusion, and randomly combine the occlusion attributes (occlusion objects, textures, and colors) to achieve a large number of more realistic occlusion types.
Multi-Scale Progressive Fusion Network for Single Image Deraining
In this work, we explore the multi-scale collaborative representation for rain streaks from the perspective of input image scales and hierarchical deep features in a unified framework, termed multi-scale progressive fusion network (MSPFN) for single image rain streak removal.
Ranked #6 on
Single Image Deraining
on Test2800
Masked Face Recognition Dataset and Application
These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed.
