The improved image inpainting algorithm via encoder and similarity constraint
Y Chen, L Liu, J Tao, R **a, Q Zhang, K Yang… - The Visual …, 2021 - Springer
Existing image inpainting algorithms based on neural network models are affected by
structural distortions and blurred textures on visible connectivity. As a result, overfitting and …
structural distortions and blurred textures on visible connectivity. As a result, overfitting and …
Simultaneous denoising and interpolation of 2D seismic data using data-driven non-negative dictionary learning
As a major concern, the existence of unwanted energy and missing traces in seismic data
acquisition can degrade interpretation of such data after processing. Instead of analytical …
acquisition can degrade interpretation of such data after processing. Instead of analytical …
Quaternion-based dictionary learning and saturation-value total variation regularization for color image restoration
Color image restoration is a critical task in imaging sciences. Most variational methods
regard the color image as a Euclidean vector or the direct combination of three monochrome …
regard the color image as a Euclidean vector or the direct combination of three monochrome …
3-D super-resolution of coded aperture millimeter-wave images using complex-valued convolutional neural network
Electromagnetic (EM) waves at millimeter-wave (mmW) frequencies have found applications
in a variety of imaging systems, from security screening to defense and automotive radars …
in a variety of imaging systems, from security screening to defense and automotive radars …
Dictionary learning for adaptive GPR landmine classification
F Giovanneschi, KV Mishra… - … on Geoscience and …, 2019 - ieeexplore.ieee.org
Ground-penetrating radar (GPR) target detection and classification is a challenging task.
Here, we consider online dictionary learning (DL) methods to obtain sparse representations …
Here, we consider online dictionary learning (DL) methods to obtain sparse representations …
A study of improved methods on image inpainting
AS Bale, SS Kumar, MS Kiran Mohan… - Trends and Advancements …, 2022 - Springer
Inpainting is the ancient art technique of modifying the image when it can't be detected. This
current study discusses the various approaches in image inpainting and compares the …
current study discusses the various approaches in image inpainting and compares the …
Automatic identification of epileptic seizures from EEG signals using sparse representation-based classification
Identifying seizure activities in non-stationary electroencephalography (EEG) is a
challenging task since it is time-consuming, burdensome, and dependent on expensive …
challenging task since it is time-consuming, burdensome, and dependent on expensive …
Sparse group bases for multisubject fMRI data
MU Khalid - IEEE Access, 2022 - ieeexplore.ieee.org
Considering that functional magnetic resonance imaging (fMRI) signals from multiple
subjects (MS) can be represented together as a sum of common and a sum of distinct rank-1 …
subjects (MS) can be represented together as a sum of common and a sum of distinct rank-1 …
Visual sentiment classification via low-rank regularization and label relaxation
In the human cognitive system, the emotional feeling is a complicated process. Visual
sentiment classification aims to predict the human emotions evoked by different images. In …
sentiment classification aims to predict the human emotions evoked by different images. In …
Dictionary learning for sparse representation of signals with hidden Markov model dependency
The goal of dictionary learning algorithms is factorizing the matrix of training signals Y with K
signals, into the dictionary matrix D and the coefficient matrix X which is a sparse matrix. The …
signals, into the dictionary matrix D and the coefficient matrix X which is a sparse matrix. The …