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Found 23 papers, 7 papers with code
Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks
Dynamic image degradations, including noise, blur and lighting inconsistencies, pose significant challenges in image restoration, often due to sensor limitations or adverse environmental conditions.
Binarized Mamba-Transformer for Lightweight Quad Bayer HybridEVS Demosaicing
Quad Bayer demosaicing is the central challenge for enabling the widespread application of Hybrid Event-based Vision Sensors (HybridEVS).
Generative Model-Assisted Demosaicing for Cross-multispectral Cameras
Specifically, our approach consists of three steps: (1) Pre-Training step: training the end-to-end neural network on a large amount of simulated data; (2) Pseudo-Pairing step: generating pseudo-labels of real target data using the self-supervised generative model; (3) Fine-Tuning step: fine-tuning the pre-trained model on the pseudo data pairs obtained in (2).
OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional Inverse Problems
The TNN-regularized optimization problem is solved by the singular value thresholding (SVT) operator, which leverages the t-SVD framework to obtain the low-rank tensor.
Random Sampling for Diffusion-based Adversarial Purification
Remarkably, under strong attack, our DiffAP even achieves a more than 20% robustness advantage with 10$\times$ sampling acceleration.
MambaSCI: Efficient Mamba-UNet for Quad-Bayer Patterned Video Snapshot Compressive Imaging
To address this challenge, we propose the MambaSCI method, which leverages the Mamba and UNet architectures for efficient reconstruction of quad-Bayer patterned color video SCI.
Cross-Scan Mamba with Masked Training for Robust Spectral Imaging
Current CNN-based methods are limited in modeling long-range dependencies, while Transformer-based models face high computational complexity.
LLEMamba: Low-Light Enhancement via Relighting-Guided Mamba with Deep Unfolding Network
Transformer-based low-light enhancement methods have yielded promising performance by effectively capturing long-range dependencies in a global context.
MIPI 2024 Challenge on Demosaic for HybridEVS Camera: Methods and Results
The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems.
Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising
Consequently the importance of HSI denoising is substantial especially for snapshot hyperspectral imaging technology.
Dual Prior Unfolding for Snapshot Compressive Imaging
Recently deep unfolding methods have achieved remarkable success in the realm of Snapshot Compressive Imaging (SCI) reconstruction.
Ranked #1 on
Spectral Reconstruction
on Real HSI
Improving Spectral Snapshot Reconstruction with Spectral-Spatial Rectification
How to effectively utilize the spectral and spatial characteristics of Hyperspectral Image (HSI) is always a key problem in spectral snapshot reconstruction.
Ranked #1 on
Spectral Reconstruction
on Real HSI
Fine-Grained Bipartite Concept Factorization for Clustering
In this paper we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph which provides comprehensive and complementary neighbor information of the data.
DiffSCI: Zero-Shot Snapshot Compressive Imaging via Iterative Spectral Diffusion Model
Specifically, firstly, we employ a pre-trained diffusion model, which has been trained on a substantial corpus of RGB images, as the generative denoiser within the Plug-and-Play framework for the first time.
MSFA-Frequency-Aware Transformer for Hyperspectral Images Demosaicing
The advantage of Maformer is that it can leverage the MSFA information and non-local dependencies present in the data.
How Image Generation Helps Visible-to-Infrared Person Re-Identification?
To this end, we propose Flow2Flow, a unified framework that could jointly achieve training sample expansion and cross-modality image generation for V2I person ReID.
Towards Complete-View and High-Level Pose-based Gait Recognition
Extensive experiments on two large gait recognition datasets, i. e., CASIA-B and OUMVLP-Pose, demonstrate that our method outperforms the baseline model and existing pose-based methods by a large margin.
Pose-Aided Video-based Person Re-Identification via Recurrent Graph Convolutional Network
Finally, we propose a dual-attention method consisting of node-attention and time-attention to obtain the temporal graph representation from the node embeddings, where the self-attention mechanism is employed to learn the importance of each node and each frame.
Multi-Granularity Graph Pooling for Video-based Person Re-Identification
To downsample the graph, we propose a multi-head full attention graph pooling (MHFAPool) layer, which integrates the advantages of existing node clustering and node selection pooling methods.
Low-rank Meets Sparseness: An Integrated Spatial-Spectral Total Variation Approach to Hyperspectral Denoising
Based on this fact, we propose a novel TV regularization to simultaneously characterize the sparsity and low-rank priors of the gradient map (LRSTV).
Log-based Sparse Nonnegative Matrix Factorization for Data Representation
Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations.
Hyperspectral Image Denoising Using Non-convex Local Low-rank and Sparse Separation with Spatial-Spectral Total Variation Regularization
In this paper, we propose a novel nonconvex approach to robust principal component analysis for HSI denoising, which focuses on simultaneously developing more accurate approximations to both rank and column-wise sparsity for the low-rank and sparse components, respectively.
Hyperspectral Image Denoising with Log-Based Robust PCA
It is a challenging task to remove heavy and mixed types of noise from Hyperspectral images (HSIs).
