Gradient histogram estimation and preservation for texture enhanced image denoising
Natural image statistics plays an important role in image denoising, and various natural
image priors, including gradient-based, sparse representation-based, and nonlocal self …
image priors, including gradient-based, sparse representation-based, and nonlocal self …
Big data dimension reduction using PCA
Principal component analysis (PCA) is a powerfultool in dimensional reduction for highly
correlated data. ClassicalPCA approaches cannot be applied to big data because ofmemory …
correlated data. ClassicalPCA approaches cannot be applied to big data because ofmemory …
Patch-based video denoising with optical flow estimation
A novel image sequence denoising algorithm is presented. The proposed approach takes
advantage of the self-similarity and redundancy of adjacent frames. The algorithm is inspired …
advantage of the self-similarity and redundancy of adjacent frames. The algorithm is inspired …
[HTML][HTML] Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction
MH Alkinani, MR El-Sakka - EURASIP journal on image and video …, 2017 - Springer
Background Digital images are captured using sensors during the data acquisition phase,
where they are often contaminated by noise (an undesired random signal). Such noise can …
where they are often contaminated by noise (an undesired random signal). Such noise can …
Nonnegative matrix factorization for the identification of EMG finger movements: Evaluation using matrix analysis
Surface electromyography (sEMG) is widely used in evaluating the functional status of the
hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The …
hand to assist in hand gesture recognition, prosthetics and rehabilitation applications. The …
Deep neuro-cognitive co-evolution for fuzzy attribute reduction by quantum leaping PSO with nearest-neighbor memeplexes
Attribute reduction with many patterns and indicators has been regarded as an important
approach for largescale data mining and machine learning tasks. However, it is extremely …
approach for largescale data mining and machine learning tasks. However, it is extremely …
Video denoising via empirical bayesian estimation of space-time patches
In this paper we present a new patch-based empirical Bayesian video denoising algorithm.
The method builds a Bayesian model for each group of similar space-time patches. These …
The method builds a Bayesian model for each group of similar space-time patches. These …
[PDF][PDF] Hyperparameter optimization in black-box image processing using differentiable proxies.
Hyperparameter Optimization in Black-box Image Processing using Differentiable Proxies
Page 1 Hyperparameter Optimization in Black-box Image Processing using Differentiable …
Page 1 Hyperparameter Optimization in Black-box Image Processing using Differentiable …
Joint spatial and spectral low-rank regularization for hyperspectral image denoising
Hyperspectral image (HSI) noise reduction is an active research topic in HSI processing due
to its significance in improving the performance for object detection and classification. In this …
to its significance in improving the performance for object detection and classification. In this …
Adaptive image denoising by targeted databases
We propose a data-dependent denoising procedure to restore noisy images. Different from
existing denoising algorithms which search for patches from either the noisy image or a …
existing denoising algorithms which search for patches from either the noisy image or a …