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Related Experiment Video

Updated: Dec 29, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising

Monagi H Alkinani¹, Mahmoud R El-Sakka²

¹1Department of Computer Science, University of Jeddah, Asfan Road, 285, Dhahban, Jeddah, 23881 Saudi Arabia.

EURASIP Journal on Image and Video Processing

|February 4, 2020

Summary

This summary is machine-generated.

Patch-based denoising methods effectively reduce noise in digital images, enhancing overall image quality. These advanced techniques represent the current state-of-the-art for image enhancement and noise reduction.

Keywords:

BM3D Bilateral filter Dictionary learning filtering Gaussian patch-PCA filtering K-SVD Non-local means filtering Patch-based image denoising Probabilistic patch-based filtering

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Visualizing Visual Adaptation

Visualizing Visual Adaptation

Published on: April 24, 2017

Area of Science:

Digital Image Processing
Computer Vision

Background:

Digital images are susceptible to noise during acquisition, transmission, or compression.
Image denoising and enhancement are critical preprocessing steps for various image processing tasks.
Patch-based denoising methods have emerged as leading approaches for additive noise reduction.

Purpose of the Study:

To investigate the application of state-of-the-art patch-based denoising methods for additive noise reduction.
To analyze the performance of these methods across diverse image datasets.

Main Methods:

Explanation of digital image noise types and denoising techniques, focusing on patch-based methods.
Quantitative and qualitative experimental evaluation of patch-based denoising performance.
Analysis of patch-based methods regarding image quality and computational efficiency.

Main Results:

Patch-based denoising methods demonstrate superior performance compared to other approaches.
Fast patch similarity measurements contribute to the development of efficient patch-based denoising algorithms.

Conclusions:

Patch-based denoising approaches are highly effective for noise reduction and image enhancement.
Patch-based denoising is identified as the current state-of-the-art in image denoising technology.