Image Denoising Using Mathematical Tools (Synthesis Lectures on Engineering, Science, and Technology) - Tapa dura

Acerca del autor

Mourad Talbi is an Associate professor in signal and image processing at the center of researches and technologies of energy (CRTEn), Tunis, Tunisia. He received his Master (2004) in automatics and Signal Processing from National School of Engineers of Tunis (ENIT) He received his Ph.D. Thesis (2010) and his HDR (2015) in Electronics from Faculty of sciences of Tunis. Actually, he is a research member of the LMEEVED Laboratory. He has made many publications in the domains of Signal and image processing and also in the domain of the photovoltaics. He also participates in many international conferences of signal and image processing and also photovoltaics. He has published many books and chapters in the domains of Signal and Image processing.

Brahim Nasraoui is an Assistant Professor in the Department of Computer Sciences at the University College of Duba, University of Tabuk, Saudi Arabia. He has conducted research in image processing, focusing on mathematical and computational approaches to improve image quality and reduce noise. His contributions include developing innovative methods that combine mathematical models with practical computational techniques.

De la contraportada

This book introduces and analyzes image denoising techniques. In the first chapter, the authors detail image denoising approaches employing Adaptive Frequency Median Filter (AFMF). In the second chapter, the authors present the first image denoising technique based on two different wavelet transforms, which are 2-D dual-tree DWT and Stationary Wavelet Transform 2-D. In the third chapter, the authors discuss the MRI Denoising based on 2-D Dual-Tree DWT and Stationary Wavelet Transform 2-D.

• Covers image denoising techniques with tools such as wavelet transforms and Adaptive Frequency Median Filter (AFMF);
• Discusses and combines Stationary Wavelet Transform 2-D and 2-D Dual-Tree DWT;
• Presents MRI Denoising using AFMF techniques.