Hybrid Machine Learning Techniques for Image Denoising Based on Wavelet Transform

Wavelet theory has been widely applied in image processing, and machine learning techniques have permeated various fields, significant improvements in image denoising remain possible. This paper introduces a novel image denoising method. It leverages wavelet transform and machine learning algorithms, presenting a synergistic approach that combines discrete wavelet transform coefficients with Random Forests and CNNs. This method significantly enhanced image clarity without increasing computational demand. This article utilized wavelet transform to extract image features, and then trained the model with wavelet coefficients as training data through machine learning methods, integrating Random Forests and Convolutional Neural Networks to analyze the effectiveness of different wavelet coefficients, thereby optimizing the model's denoising performance. It has been proved effective in denoising performance after rigorous validation through quantitative comparisons of Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) on the high-quality stereo image dataset Flickr1024.