Multi-Scale Based Approach for Denoising Real-World Noisy Image Using Curvelet Thresholding: Scope and Beyond-Reference-Cited by-同舟云学术

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Multi-Scale Based Approach for Denoising Real-World Noisy Image Using Curvelet Thresholding: Scope and Beyond

Published:2024 Issue: Volume:12 Page:25090-25105
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Panigrahi Susant Kumar¹^ORCID,Tripathy Santosh Kumar²,Bhowmick Anirban¹,Satapathy Santosh Kumar³,Barsocchi Paolo⁴^ORCID,Bhoi Akash Kumar⁴^ORCID

Affiliation:

1. School of Electrical and Electronics Engineering, VIT Bhopal University, Kothri Kalan, Sehore, Madhya Pradesh, India

2. School of Computing Science and Engineering, VIT Bhopal University, Kothri Kalan, Sehore, Madhya Pradesh, India

3. Information and Communication Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

4. Institute of Information Science and Technologies, National Research Council, Pisa, Italy

Funder

CHIPS project “Cyber and Human Intelligence for Physical Systems”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10430182.pdf?arnumber=10430182

Reference66 articles.

1. Learning to See in the Dark

2. Image denoising by edge preserved curvelet thresholding;Panigrahi,2019

3. Real Image Denoising With Feature Attention

4. Weighted Nuclear Norm Minimization with Application to Image Denoising

5. From learning models of natural image patches to whole image restoration

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1. Real-world image denoising via efficient diffusion model with controllable noise generation;Journal of Electronic Imaging;2024-07-03

2. Alzheimer’s Disease Detection via Multiscale Feature Modelling Using Improved Spatial Attention Guided Depth Separable CNN;International Journal of Computational Intelligence Systems;2024-05-07

3. An FPGA-Based Hardware Architecture of Gaussian-Adaptive Bilateral Filter for Real-Time Image Denoising;IEEE Access;2024

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