Image Denoising Using Autoencoder: Utilizing Deep Learning and Autoencoder Techniques to Enhance Natural Images by Eliminating Noisy Pixels and Grains

Digital pictures play a critical role in our everyday life and are used in multiple fields from computer vision to medical reports. But sometimes these pictures suffer from interfering noises, as a consequence of defective sensors or interference due to transmission. Analyzing these noisy pictures can lead to misguided results, highlighting the need for development of effective denoising techniques. Through this research we propose a Convolutional autoencoder-based image denoising technique. Using a set of training images, autoencoders are trained to recognize and understand noise patterns, and this learning is used to effectively remove noise from new unrecognized images. Different type of noises is added to training set at different variance levels. The resultant denoised images are evaluated through both qualitative and quantitative measure. Results showed that this novel approach outperforms the performance of traditional image denoising methods, obtaining high-quality results, as measured by PSNR and SSIM values. This development of image denoising technology has not only raised the quality (or value) of digital images it can also increase reliability with respect to accuracy-dependent analysis for various fields.