Abstract: The performance of image preprocessing algorithms is directly related to the effect of image post-processing,such as image segmentation,target detection,edge extraction,etc.In order to obtain high-quality digital images,image noise reduction has become an essential pre-step.Image noise reduction aims to maintain the integrity of the original information(i.e.,the main features) as much as possible,while being able to remove the useless information in the signal.To this end,this paper proposes a lossy compression coding based convolutional auto-encoders(AutoEnconders,AE) denoising model.According to the principle of maximal coding rate reduction(MCR²),a new loss function is designed to replace the mean squared error(MSE) loss commonly used in mainstream deep learning algorithms to improve the robustness and adaptability of the model.The model first processes the noisy image through an encoder to obtain the hidden variables,and then decodes it using a decoder to remove the noise and obtain the reconstructed image.Next,keeping the encoder unchanged,the reconstructed image is fed into the encoder so that the encoder continues to learn and obtains the reconstructed hidden variables.Finally,the error between the reconstructed image and the original image is indirectly measured by calculating the distance between the hidden variable and the reconstructed hidden variable,which is used as the convergence cost for model training.The proposed model is validated extensively on thumbnails128x128 and CBSD68 datasets,and the experimental results show that the self-encoder framework(AE-MCR²) exhibits good performance under different types of noise(Gaussian,Bernoulli,and Poisson) and has some interpretability.

Key words: Computer vision, Image denoising, Autoencoder, Convolutional neural network, Compression coding