Image Restoration Using RBM

Abstract

Image restoration is a critical task in computer vision, aiming to recover degraded images caused by noise, missing pixels, or corruption. Restricted Boltzmann Machines (RBMs), a type of unsupervised neural network, have gained popularity for their ability to learn hidden representations and restore images effectively. This paper provides a review of existing research and projects related to image restoration using RBMs and other deep learning techniques. It highlights the key approaches, algorithms, and outcomes in this field, providing a comparative perspective on their effectiveness.

Introduction

Image restoration is vital in improving degraded images for applications like historical photo recovery, satellite imagery, and medical imaging. Restricted Boltzmann Machines (RBMs), a type of generative neural network, have proven effective in learning the underlying structure of images and reconstructing missing or corrupted parts without requiring labeled data. RBMs consist of visible and hidden layers and are trained using Contrastive Divergence to model the probability distribution of uncorrupted images.

Recent research has introduced variations such as Structural RBMs, which account for spatial relationships to better preserve local image features, and novel training algorithms tailored to corrupted data for improved restoration accuracy. Surveys indicate that while Convolutional Neural Networks (CNNs) often outperform other models in supervised settings, RBMs remain valuable for unsupervised tasks due to their efficiency and ability to capture latent features without extensive labeling.

The proposed methodology employs an RBM with 784 visible units and 500 hidden units to restore binary images from datasets like MNIST, using Contrastive Divergence and stochastic gradient descent for training. Experiments conducted on a Python environment with GPU acceleration demonstrated that RBMs can successfully reconstruct corrupted images by leveraging learned representations.

While RBMs have limitations such as training complexity and convergence issues, they remain relevant, especially when combined with other models like CNNs for enhanced performance. The study’s RBM model shows competitive restoration quality with lower computational complexity compared to Structural RBMs, making it suitable for resource-constrained settings.

Conclusion

Restricted Boltzmann Machines continue to play a significant role in image restoration, especially in situations where training data is limited or unsupervised learning is necessary. Their ease of implementation and low computational cost make them attractive for certain applications, particularly in academic and lightweight real-time systems. Although newer methods such as GANs and diffusion models often outperform RBMs in quality and flexibility, RBMs remain a strong foundational tool for image restoration. With advancements in hybrid modeling, optimization techniques, and integration with newer architectures, RBMs can still contribute meaningfully to solving complex image restoration challenges. Future research should focus on scalability, adaptability to different restoration tasks, and incorporation into broader image processing frameworks.

References

[1] Bidaurrazaga, A., et al., Structural Restricted Boltzmann Machine for Image Denoising and Classification, arXiv, 2023. [2] Fakhari, A., Kiani, K., A New RBM Training Algorithm for Image Restoration, 2021. [3] Su, J., Xu, B., Yin, H., A Survey of Deep Learning Approaches to Image Restoration, 2022. [4] Overview on Restricted Boltzmann Machines, ScienceDirect. [5] Diffusion Models for Image Restoration and Enhancement, arXiv, 2023. [6] A Survey on All-in-One Image Restoration, arXiv, 2024. [7] Hybrid Models Combining RBMs and CNNs for Image Restoration, IEEE Transactions on Neural Networks, 2022. [8] RBM-Based Restoration of Historical Documents, Journal of Image and Vision Computing, 2021. [9] H. Li, T. Zhang, Hybrid Models Combining RBMs and CNNs for Image Restoration, IEEE Transactions on Neural Networks and Learning Systems, Vol. 33, No. 4, pp. 1893–1905, 2022.

Copyright

Copyright © 2025 Vrushali Kondhalkar, Vedant Jeurkar, Tanaji Dhavale, Jyotiraditya Jadhav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.