Image Data Denoising Using the unsupervised learning Model of RBM

 The application of restricted Boltzmann machine (RBM) methods in image denoising is still unknown, it can learn the probability distribution of image data unsupervised. Based on the previous research of RBM theory and related algorithms, a new denoising method based on the RBM model can remove the random noise in image data. The advantage is that it does not require training sets or pre-training for denoising. It is efficient and effective in denoising. The original image is added with random noise, and the noisy image is divided into several small blocks, which are pulled into vectors one by one, and then input into the deep belief nets (DBN) with multiple RBM models to learning in batches. The original image is used as a label for fine-tuning, and finally the output is restored to the image, so as to eliminate the random noise. The RBM model algorithm is used to evaluate the effectiveness of removing the random noise in the image data, simulated seismic data and real seismic data. The experimental results show that the proposed denoising method using RBM model is feasible in image data and seismic data, and the effects of removing random noise is better than other unsupervised learning algorithms and some denoising methods with deep learning, such as convolutional neural networks. Furthermore, it shows that the RBM model has a strong performance in image feature learning and essential feature extraction. It also provides a new research idea and reference for the methods of image denoising in the engineering fields.