Exploring Convolutional Autoencoder Efficacy in Noise Removal for Image Processing and Computer Vision: A Study Using the MNIST Dataset

Noise removal in image processing and computer vision is a crucial preprocessing step employing a spectrum of techniques. In recent years, autoencoders exhibit remarkable efficacy in mapping noisy images to clean counterparts, capturing intricate relationships for effective noise removal. Motivated by the challenges posed by noise in real-world images, this research focuses on the denoising preprocessing step, crucial for tasks like object detection and segmentation. The study explores the application of autoencoders in removing artificially added noise from images within the MNIST dataset. The MNIST dataset’s simplicity and historical significance facilitate focused examinations on specific aspects, such as the impact of different types and levels of noise. The efficacy of autoencoders for noise removal is assessed through the evaluation of results using various metrics, including SSIM, PSNR, MSE, and RMSE. In one remarkable instance, the reconstruction process achieved an impressive peak SSIM score of 99.06%, showcasing the efficacy of the method in preserving image fidelity despite the challenging presence of noise. This comprehensive analysis provides valuable insights into the performance and effectiveness of autoencoders in the context of noise reduction in various domains.