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Found 12 papers, 2 papers with code
Unmixing Diffusion for Self-Supervised Hyperspectral Image Denoising
Consequently the importance of HSI denoising is substantial especially for snapshot hyperspectral imaging technology.
Degradation-Noise-Aware Deep Unfolding Transformer for Hyperspectral Image Denoising
However, model-based approaches rely on hand-crafted priors and hyperparameters, while learning-based methods are incapable of estimating the inherent degradation patterns and noise distributions in the imaging procedure, which could inform supervised learning.
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.
Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor
DJRD includes a newly designed Quad Bayer remosaicing (QB-Re) block, integrated denoising modules based on Swin-transformer and multi-scale wavelet transform.
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).
Gradient Variance Loss for Structure-Enhanced Image Super-Resolution
Further, we minimize the distance between the computed variance maps to enforce the model to produce high variance gradient maps that will lead to the generation of high-resolution images with sharper edges.
Resolution based Feature Distillation for Cross Resolution Person Re-Identification
Most of the existing approaches resolve the re-id task as low resolution problem in which a low resolution query image is searched in a high resolution images gallery.
Matrix Completion With Variational Graph Autoencoders: Application in Hyperlocal Air Quality Inference
Existing inference methods typically use low spatial resolution data collected by fixed monitoring stations and infer the concentration of air pollutants using additional types of data, e. g., meteorological and traffic information.
Domain Adaptive Segmentation in Volume Electron Microscopy Imaging
In this work, we extend recently proposed classification DA techniques to an encoder-decoder layout and propose a novel method that adds a reconstruction decoder to the classical encoder-decoder segmentation in order to align source and target encoder features.
Bayesian Deconvolution of Scanning Electron Microscopy Images Using Point-spread Function Estimation and Non-local Regularization
Microscopy is one of the most essential imaging techniques in life sciences.
Convolutional Neural Network Pruning to Accelerate Membrane Segmentation in Electron Microscopy
Biological membranes are one of the most basic structures and regions of interest in cell biology.
Point Triangulation Through Polyhedron Collapse Using the l[?] Norm
Multi-camera triangulation of feature points based on a minimisation of the overall L2 reprojection error can get stuck in suboptimal local minima or require slow global optimisation.
