[HTML][HTML] Applications of machine vision in pharmaceutical technology: A review
The goal of this paper is to give an introduction to analysis of images acquired by a digital
camera with visible illumination and to review its applications as a Process Analytical …
camera with visible illumination and to review its applications as a Process Analytical …
Learning nonlocal sparse and low-rank models for image compressive sensing: Nonlocal sparse and low-rank modeling
The compressive sensing (CS) scheme exploits many fewer measurements than suggested
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
by the Nyquist–Shannon sampling theorem to accurately reconstruct images, which has …
Deep generalized unfolding networks for image restoration
Deep neural networks (DNN) have achieved great success in image restoration. However,
most DNN methods are designed as a black box, lacking transparency and interpretability …
most DNN methods are designed as a black box, lacking transparency and interpretability …
ISTA-Net: Interpretable optimization-inspired deep network for image compressive sensing
With the aim of develo** a fast yet accurate algorithm for compressive sensing (CS)
reconstruction of natural images, we combine in this paper the merits of two existing …
reconstruction of natural images, we combine in this paper the merits of two existing …
Depth image denoising using nuclear norm and learning graph model
Depth image denoising is increasingly becoming the hot research topic nowadays, because
it reflects the three-dimensional scene and can be applied in various fields of computer …
it reflects the three-dimensional scene and can be applied in various fields of computer …
Rank minimization for snapshot compressive imaging
Snapshot compressive imaging (SCI) refers to compressive imaging systems where multiple
frames are mapped into a single measurement, with video compressive imaging and …
frames are mapped into a single measurement, with video compressive imaging and …
Plug-and-play ADMM for image restoration: Fixed-point convergence and applications
Alternating direction method of multiplier (ADMM) is a widely used algorithm for solving
constrained optimization problems in image restoration. Among many useful features, one …
constrained optimization problems in image restoration. Among many useful features, one …
Image compressed sensing using convolutional neural network
In the study of compressed sensing (CS), the two main challenges are the design of
sampling matrix and the development of reconstruction method. On the one hand, the …
sampling matrix and the development of reconstruction method. On the one hand, the …
Dynamic attentive graph learning for image restoration
Non-local self-similarity in natural images has been verified to be an effective prior for image
restoration. However, most existing deep non-local methods assign a fixed number of …
restoration. However, most existing deep non-local methods assign a fixed number of …
A survey of sparse representation: algorithms and applications
Sparse representation has attracted much attention from researchers in fields of signal
processing, image processing, computer vision, and pattern recognition. Sparse …
processing, image processing, computer vision, and pattern recognition. Sparse …