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Main Authors: Singh, Digvijay, Shukla, Rahul, Singh, Karunesh Kumar
Format: Preprint
Published: 2025
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Online Access:https://arxiv.org/abs/2508.14043
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author Singh, Digvijay
Shukla, Rahul
Singh, Karunesh Kumar
author_facet Singh, Digvijay
Shukla, Rahul
Singh, Karunesh Kumar
contents This research includes the study of some positive sampling Kantorovich operators (SK operators) and their convergence properties. A comprehensive analysis of both local and global approximation properties is presented using sampling Kantorovich (SK), Gaussian, Bilateral and the thresholding wavelet-based operators in the framework of SK-operators. Explicitly, we start the article by introducing the basic terminology and state the fundamental theorem of approximation (FTA) by imposing the various required conditions corresponding to the various defined operators. We measure the error and study the other mathematical parameters such as the mean square error (MSE), the speckle index (SI), the speckle suppression index (SSI), the speckle mean preservation index (SMPI), and the equivalent number of looks (ENL) at various levels of resolution parameters. The nature of these operators are demonstrated via an example under ideal conditions in tabulated form at a certain level of samples. Eventually, another numerical example is illustrated to discuss the region of interest (ROI) via SI, SSI and SMPI of 2D Shepp-Logan Phantom taken slice from the 3D image, which gives the justification of the fundamental theorem of approximation (FTA). At the end of the derivation and illustrations we observe that the various operators have their own significance while studying the various features of the image because of the uneven nature of an image (non-ideal condition). Therefore, to some extent, some operators work well and some do not for some specific features of the image.
format Preprint
id arxiv_https___arxiv_org_abs_2508_14043
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A comparative study of some wavelet and sampling operators on various features of an image
Singh, Digvijay
Shukla, Rahul
Singh, Karunesh Kumar
Computer Vision and Pattern Recognition
Functional Analysis
41A25, 41A35, 46E30, 47A58, 47B38, 94A12
This research includes the study of some positive sampling Kantorovich operators (SK operators) and their convergence properties. A comprehensive analysis of both local and global approximation properties is presented using sampling Kantorovich (SK), Gaussian, Bilateral and the thresholding wavelet-based operators in the framework of SK-operators. Explicitly, we start the article by introducing the basic terminology and state the fundamental theorem of approximation (FTA) by imposing the various required conditions corresponding to the various defined operators. We measure the error and study the other mathematical parameters such as the mean square error (MSE), the speckle index (SI), the speckle suppression index (SSI), the speckle mean preservation index (SMPI), and the equivalent number of looks (ENL) at various levels of resolution parameters. The nature of these operators are demonstrated via an example under ideal conditions in tabulated form at a certain level of samples. Eventually, another numerical example is illustrated to discuss the region of interest (ROI) via SI, SSI and SMPI of 2D Shepp-Logan Phantom taken slice from the 3D image, which gives the justification of the fundamental theorem of approximation (FTA). At the end of the derivation and illustrations we observe that the various operators have their own significance while studying the various features of the image because of the uneven nature of an image (non-ideal condition). Therefore, to some extent, some operators work well and some do not for some specific features of the image.
title A comparative study of some wavelet and sampling operators on various features of an image
topic Computer Vision and Pattern Recognition
Functional Analysis
41A25, 41A35, 46E30, 47A58, 47B38, 94A12
url https://arxiv.org/abs/2508.14043