Search
Search Results
-
Towards Off-the-grid Algorithms for Total Variation Regularized Inverse Problems
We introduce an algorithm to solve linear inverse problems regularized with the total (gradient) variation in a gridless manner. Contrary to most...
-
Combined higher order non-convex total variation with overlap** group sparsity for impulse noise removal
A typical approach to eliminate impulse noise is to use the ℓ 1 -norm for both the data fidelity term and the regularization terms. However, the ℓ 1 -norm...
-
Total generalized variation-based Retinex image decomposition
Human visual system (HVS) can perceive color under varying illumination conditions, and Retinex theory is precisely aimed to simulate and explain how...
-
Data minimization for GDPR compliance in machine learning models
The EU General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA) mandate the principle of data minimization , which...
-
Missing Elements Recovery Using Low-Rank Tensor Completion and Total Variation Minimization
The Low-rank (LR) and total variation (TV) are two most popular regularizations for image processing problems and have sparked a tremendous number of...
-
Nonlocal adaptive direction-guided structure tensor total variation for image recovery
A common strategy in variational image recovery is utilizing the nonlocal self-similarity property, when designing energy functionals. One such...
-
NashAE: Disentangling Representations Through Adversarial Covariance Minimization
We present a self-supervised method to disentangle factors of variation in high-dimensional data that does not rely on prior knowledge of the...
-
Impact of Total Variation Regularization on Character Segmentation from Historical Stone Inscriptions
AbstractAn automatic segmentation scheme for accurate segmentation of characters from Historical Handwritten Kannada Stone Inscription images is...
-
SoftClusterMix: learning soft boundaries for empirical risk minimization
Deep convolutional networks are data hungry learners and, to compensate for the limited amount of available data, various augmentation methods have...
-
Proximal alternating minimization method for adaptive TGV-based image restoration
This article presents an adaptive total generalized variation regularized strategy for image reconstruction. Unlike the traditional fixed weights...
-
Experimental Study of Algorithms for Minimization of Binary Decision Diagrams Using Algebraic Representations of Cofactors
AbstractBinary decision diagram (BDD) is used for technology-independent optimization, performed as the first stage in the synthesis of logic...
-
Particle swarm optimization technique for speed control and torque ripple minimization of switched reluctance motor using PID and FOPID controllers
Switched Reluctance Motors has become one of the best solutions for EV applications because of its numerous benefits over other electric drive...
-
Image smog restoration using oblique gradient profile prior and energy minimization
Removing the smog from digital images is a challenging pre-processing tool in various imaging systems. Therefore, many smog removal (i.e.,...
-
Adaptive total variation and second-order total variation-based model for low-rank tensor completion
Recently, low-rank regularization has achieved great success in tensor completion. However, only considering the global low-rankness is not...
-
Optimizing operational parameters through minimization of running costs for shared mobility public transit service: an application of decision tree models
The aim of this study was to use machine learning model for prediction of running costs of public transport buses in Karachi, which is the most...
-
Multi-Directional Total Variation and Wavelet Transform Based Methods: Application for Correlation Fringe Patterns Denoising and Demodulation
In this work, we present a multi-directional total variation method to reduce the high frequency speckle noise in order to prepare the digital...
-
Automated Data-Driven Selection of the Hyperparameters for Total-Variation-Based Texture Segmentation
Penalized least squares are widely used in signal and image processing. Yet, it suffers from a major limitation since it requires fine-tuning of the...
-
Discrete and combinatorial gravitational search algorithms for test case prioritization and minimization
Regression testing is an essential but expensive activity to re-execute all the test cases every time the software updates. Test case prioritization...
-
-
POC-net: pelican optimization-based convolutional neural network for recognizing large pose variation from video
Nowadays, face recognition using video surveillance systems becomes one of the active research topics in security domains. Security plays a...
