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Found 43 papers, 20 papers with code
Denoising Diffusion Semantic Segmentation with Mask Prior Modeling
In this paper, we propose to ameliorate the semantic segmentation quality of existing discriminative approaches with a mask prior modeled by a recently-developed denoising diffusion generative model.
Instance Segmentation in the Dark
Existing instance segmentation techniques are primarily tailored for high-visibility inputs, but their performance significantly deteriorates in extremely low-light environments.
Spectral Enhanced Rectangle Transformer for Hyperspectral Image Denoising
In this paper, we address these issues by proposing a spectral enhanced rectangle Transformer, driving it to explore the non-local spatial similarity and global spectral low-rank property of HSIs.
LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising
Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blind-spot networks (BSNs).
Hybrid Spectral Denoising Transformer with Learnable Query
Challenges in adapting transformer for HSI arise from the capabilities to tackle existing limitations of CNN-based methods in capturing the global and local spatial-spectral correlations while maintaining efficiency and flexibility.
HyperAttack: Multi-Gradient-Guided White-box Adversarial Structure Attack of Hypergraph Neural Networks
Hypergraph neural networks (HGNN) have shown superior performance in various deep learning tasks, leveraging the high-order representation ability to formulate complex correlations among data by connecting two or more nodes through hyperedge modeling.
Hyperspectral Image Super Resolution with Real Unaligned RGB Guidance
The features of RGB reference images are then processed by a multi-stage alignment module to explicitly align the features of RGB reference with the LR HSI.
Mixed Attention Network for Hyperspectral Image Denoising
However, existing methods show limitations in exploring the spectral correlations across different bands and feature interactions within each band.
Dynamic Aggregated Network for Gait Recognition
Specifically, we create a dynamic attention mechanism between the features of neighboring pixels that not only adaptively focuses on key regions but also generates more expressive local motion patterns.
Improved Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
Hyperspectral image is unique and useful for its abundant spectral bands, but it subsequently requires extra elaborated treatments of the spatial-spectral correlation as well as the global correlation along the spectrum for building a robust and powerful HSI restoration algorithm.
Spatial-Spectral Transformer for Hyperspectral Image Denoising
Hyperspectral image (HSI) denoising is a crucial preprocessing procedure for the subsequent HSI applications.
GTAV-NightRain: Photometric Realistic Large-scale Dataset for Night-time Rain Streak Removal
In this paper, we propose GTAV-NightRain dataset, which is a large-scale synthetic night-time rain streak removal dataset.
Deep Plug-and-Play Prior for Hyperspectral Image Restoration
Deep-learning-based hyperspectral image (HSI) restoration methods have gained great popularity for their remarkable performance but often demand expensive network retraining whenever the specifics of task changes.
ProbNVS: Fast Novel View Synthesis with Learned Probability-Guided Sampling
Existing state-of-the-art novel view synthesis methods rely on either fairly accurate 3D geometry estimation or sampling of the entire space for neural volumetric rendering, which limit the overall efficiency.
Estimating Fine-Grained Noise Model via Contrastive Learning
In this work, we combine both noise modeling and estimation, and propose an innovative noise model estimation and noise synthesis pipeline for realistic noisy image generation.
End-to-End Video Text Spotting with Transformer
Recent video text spotting methods usually require the three-staged pipeline, i. e., detecting text in individual images, recognizing localized text, tracking text streams with post-processing to generate final results.
Mutual Contrastive Low-rank Learning to Disentangle Whole Slide Image Representations for Glioma Grading
In this proposed scheme, we design a normalized modality contrastive loss (NMC-loss), which could promote to disentangle multi-modality complementary representation of FFPE and frozen sections from the same patient.
Physics-based Noise Modeling for Extreme Low-light Photography
Enhancing the visibility in extreme low-light environments is a challenging task.
Ranked #2 on
Image Denoising
on SID x300
Dynamic Proximal Unrolling Network for Compressive Imaging
In this paper, a dynamic proximal unrolling network (dubbed DPUNet) was proposed, which can handle a variety of measurement matrices via one single model without retraining.
Learning Temporal Consistency for Low Light Video Enhancement From Single Images
Based on this idea, we propose our method which can infer motion prior for single image low light video enhancement and enforce temporal consistency.
Learning To Reconstruct High Speed and High Dynamic Range Videos From Events
Event cameras are novel sensors that capture the dynamics of a scene asynchronously.
LocalTrans: A Multiscale Local Transformer Network for Cross-Resolution Homography Estimation
By combining the local transformer with the multiscale structure, the network is able to capture long-short range correspondences efficiently and accurately.
Disentangled Face Attribute Editing via Instance-Aware Latent Space Search
We further propose a Disentanglement-Transformation (DT) metric to quantify the attribute transformation and disentanglement efficacy and find the optimal control factor between attribute-level and instance-specific directions based on it.
Probing quasi-long-range ordering by magnetostriction in monolayer CoPS3
Mermin-Wagner-Coleman theorem predicts no long-range magnetic order at finite temperature in the two-dimensional (2D) isotropic systems, but a quasi-long-range order with a divergent correlation length at the Kosterlitz-Thouless (KT) transition for planar magnets.
Mesoscale and Nanoscale Physics
Hyperspectral Image Denoising With Realistic Data
On the other hand, we propose an accurate HSI noise model which matches the distribution of real data well and can be employed to synthesize realistic dataset.
Weakly-supervised Semantic Segmentation in Cityscape via Hyperspectral Image
It is based on the idea that high-resolution HSIs in city scenes contain rich spectral information, which can be easily associated to semantics without manual labeling.
Semi-Supervised Semantic Segmentation
Weakly supervised Semantic Segmentation
+1
GPS-Net: Graph-based Photometric Stereo Network
For all-pixel operation, we propose the Normal Regression Network to make efficient use of the intra-image spatial information for predicting a surface normal map with rich details.
Cross-MPI: Cross-scale Stereo for Image Super-Resolution using Multiplane Images
Various combinations of cameras enrich computational photography, among which reference-based superresolution (RefSR) plays a critical role in multiscale imaging systems.
TFPnP: Tuning-free Plug-and-Play Proximal Algorithm with Applications to Inverse Imaging Problems
In this work, we present a class of tuning-free PnP proximal algorithms that can determine parameters such as denoising strength, termination time, and other optimization-specific parameters automatically.
Partial FC: Training 10 Million Identities on a Single Machine
The experiment demonstrates no loss of accuracy when training with only 10\% randomly sampled classes for the softmax-based loss functions, compared with training with full classes using state-of-the-art models on mainstream benchmarks.
Ranked #2 on
Face Identification
on MegaFace
EasyQuant: Post-training Quantization via Scale Optimization
The 8 bits quantization has been widely applied to accelerate network inference in various deep learning applications.
A Physics-based Noise Formation Model for Extreme Low-light Raw Denoising
Lacking rich and realistic data, learned single image denoising algorithms generalize poorly to real raw images that do not resemble the data used for training.
Ranked #1 on
Image Denoising
on ELD SonyA7S2 x100
3D Quasi-Recurrent Neural Network for Hyperspectral Image Denoising
In this paper, we propose an alternating directional 3D quasi-recurrent neural network for hyperspectral image (HSI) denoising, which can effectively embed the domain knowledge -- structural spatio-spectral correlation and global correlation along spectrum.
Tuning-free Plug-and-Play Proximal Algorithm for Inverse Imaging Problems
Moreover, we discuss the practical considerations of the plugged denoisers, which together with our learned policy yield state-of-the-art results.
Hyperspectral City V1.0 Dataset and Benchmark
This document introduces the background and the usage of the Hyperspectral City Dataset and the benchmark.
Hyperspectral Image Super-Resolution With Optimized RGB Guidance
To overcome the limitations of existing hyperspectral cameras on spatial/temporal resolution, fusing a low resolution hyperspectral image (HSI) with a high resolution RGB (or multispectral) image into a high resolution HSI has been prevalent.
Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements
Removing undesirable reflections from a single image captured through a glass window is of practical importance to visual computing systems.
Ranked #1 on
Reflection Removal
on SIR^2(Objects)
Joint Camera Spectral Sensitivity Selection and Hyperspectral Image Recovery
Hyperspectral image (HSI) recovery from a single RGB image has attracted much attention, whose performance has recently been shown to be sensitive to the camera spectral sensitivity (CSS).
Image Restoration from Patch-based Compressed Sensing Measurement
A series of methods have been proposed to reconstruct an image from compressively sensed random measurement, but most of them have high time complexity and are inappropriate for patch-based compressed sensing capture, because of their serious blocky artifacts in the restoration results.
Exploiting Spectral-Spatial Correlation for Coded Hyperspectral Image Restoration
In this paper, we propose an effective method for coded hyperspectral image restoration, which exploits extensive structure sparsity in the hyperspectral image.
Adaptive Spatial-Spectral Dictionary Learning for Hyperspectral Image Denoising
Hyperspectral imaging is beneficial in a diverse range of applications from diagnostic medicine, to agriculture, to surveillance to name a few.
Separating Fluorescent and Reflective Components by Using a Single Hyperspectral Image
This paper introduces a novel method to separate fluorescent and reflective components in the spectral domain.
Reflectance and Fluorescent Spectra Recovery based on Fluorescent Chromaticity Invariance under Varying Illumination
We then show that given the spectral reflectance and fluorescent chromaticity, the fluorescence absorption and emission spectra can also be estimated.
