Image Denoising using Wavelet Transformer

Abstract

As digital imaging becomes increasingly important in various fields, the demand for effective methods to reduce image noise has risen. This study explores a wide range of techniques for denoising images, including both traditional and modern methods. It examines classical filters, statistical methods, and contemporary machine learning algorithms, explaining their principles, strengths, and weaknesses. Through a systematic review of existing literature, these techniques are categorized based on their underlying approaches and practical uses. Comparative analyses offer insights into the advantages and drawbacks of each method. Additionally, the paper discusses current trends and future directions in image denoising research. This comprehensive study serves as a valuable resource for researchers, professionals, and enthusiasts seeking a deep understanding of the evolving field of image denoising.