Abstract: The existing denoising methods for restricted Boltzmann machines (RBM) models are entirely based on their being undirected graphical models, when multiple RBM combination models are used, the distribution of the lower layer depends on the distribution of the higher layer. This leads to high computational complexity and subpar denoising performance and makes it difficult to generalize applications. To solve this problem, we propose a method for random noise removal from image data based on deep belief nets (DBN) with RBM models. The advantage of this method lies in the fact that the distribution of the lower layers does not depend on the distribution of the higher layers. Moreover, during the denoising process, it does not require training sets or pre-training, making it efficient and highly 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 DBN model with two hidden layers to learn 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 DBN model algorithm is used to evaluate the effectiveness of removing random noise in the image data, simulated seismic data and real seismic data. The experimental results show that the proposed denoising method using DBN model is feasible in image data and seismic data. When the standard deviation of noise is 50 dB, the peak signal-to-noise ratio of the denoised images is improved by at least 2.08 dB and at least 6.99% on the Set12 dataset, and under different noise standard deviations, the proposed method outperforms other unsupervised learning algorithms and deep learning methods such as convolutional neural networks in removing random noise. This demonstrates 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 image denoising methods in the engineering fields.