Hyperspectral image denoising: From model-driven, data-driven, to model-data-driven
Mixed noise pollution in HSI severely disturbs subsequent interpretations and applications.
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
In this technical review, we first give the noise analysis in different noisy HSIs and conclude …
Hyperspectral image restoration: Where does the low-rank property exist
Y Chang, L Yan, B Chen, S Zhong… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
Hyperspectral image (HSI) restoration is to recover the clean image from degraded version,
such as the noisy, blurred, or damaged. Recent low-rank tensor-based recovery methods …
such as the noisy, blurred, or damaged. Recent low-rank tensor-based recovery methods …
Multi-dimensional visual data completion via low-rank tensor representation under coupled transform
This paper addresses the tensor completion problem, which aims to recover missing
information of multi-dimensional images. How to represent a low-rank structure embedded …
information of multi-dimensional images. How to represent a low-rank structure embedded …
Recent developments in computational color image denoising with PDEs to deep learning: a review
Image denoising methods are of fundamental importance in image processing and artificial
intelligence systems. In this review, we analyze the traditional and state of the art …
intelligence systems. In this review, we analyze the traditional and state of the art …
A low-rank tensor dictionary learning method for hyperspectral image denoising
As a 3-order tensor, a hyperspectral image (HSI) has dozens of spectral bands, which can
deliver more information of real scenes. However, real HSIs are often corrupted by noises in …
deliver more information of real scenes. However, real HSIs are often corrupted by noises in …
Quaternion nuclear norm minus frobenius norm minimization for color image reconstruction
Color image restoration methods typically represent images as vectors in Euclidean space
or combinations of three monochrome channels. However, they often overlook the …
or combinations of three monochrome channels. However, they often overlook the …
Unidirectional video denoising by mimicking backward recurrent modules with look-ahead forward ones
While significant progress has been made in deep video denoising, it remains very
challenging for exploiting historical and future frames. Bidirectional recurrent networks …
challenging for exploiting historical and future frames. Bidirectional recurrent networks …
Multi-channel nuclear norm minus Frobenius norm minimization for color image denoising
Color image denoising is frequently encountered in various image processing and computer
vision tasks. One traditional strategy is to convert the RGB image to a less correlated color …
vision tasks. One traditional strategy is to convert the RGB image to a less correlated color …
Hyperspectral image denoising via weighted multidirectional low-rank tensor recovery
Recently, low-rank tensor recovery methods based on subspace representation have
received increased attention in the field of hyperspectral image (HSI) denoising …
received increased attention in the field of hyperspectral image (HSI) denoising …
3D geometrical total variation regularized low-rank matrix factorization for hyperspectral image denoising
The total variation (TV) regularization has been widely used in hyperspectral image (HSI)
denoising owing to its powerful capabilities in terms of structure preservation. However …
denoising owing to its powerful capabilities in terms of structure preservation. However …