Denoising Method for Infrared Images of Dry-type Hollow Reactors Based on Improved Wavelet Transform and Convolutional Neural Network
A denoising method for infrared images of dry hollow reactors based on improved wavelet transform and convolutional neural network(CNN)was proposed to address the issue of unsatisfactory noise removal in traditional wavelet transform methods.Firstly,residual learning in convolutional neural networks was used to extract mixed feature information from images;then,the image was decomposed by improving the wavelet transform,and the decomposed components were input into the network for training;furthermore,residual learning was used to enhance the texture details of images,solving the shortcomings of traditional image denoising methods;finally,a simulation comparison was conducted.The results show that the proposed method can reduce the difficulty of network computation,accelerate training speed with good denoising performance,which is superior to traditional image denoising methods.