Hybrid frequency-domain and machine learning framework for image noise classification and adaptive restoration
Year: 2026
Type: article
Abstract: Classification and restoration of envision noise are important problems in digital image processing that affect computer vision, medical imaging, and multimedia applications.This paper presents a sophisticated framework that addresses many noise types, such as Gaussian noise, impulse noise, motion blur, and speckle noise, by combining frequency-domain analysis, machine learning, and customized restoration algorithms.The method captures unique noise signatures by using the Discrete Cosine Transform (DCT), Discrete Fourier Transform (DFT) and the Fast Fourier Transform (FFT) to extract subtle frequency-domain properties.Several classifiers are investigated to achieve accurate noise type detection.A thorough assessment of the framework's flexibility and scalability is provided by this dataset, which includes a variety of distortions and noise levels.Adaptive, noise-specific filtering algorithms that are in line with the recognized noise type are used by the system for noise restoration.A combination of bilateral and Gaussian filters is used to handle Gaussian noise, and sophisticated median and outlier-suppression
DOI: 10.47974/jios-2163
Authors: Aakanksha Jain, Harshal Arolkar
Institution: GLS University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W7203665010