Exploring Advanced Image Refinement with Blind Denoising Autoencoder Architecture

This project introduces an innovative method for enhancing license plate images by employing a blind autoencoder-based denoising and deblurring technique. Unlike conventional approaches that heavily rely on labeled training data, which can be both scarce and costly to obtain, our method circumvents this requirement. Instead, it leverages a blind autoencoder trained on a vast collection of unlabeled license plate images to effectively denoise and deblur input images. Through evaluation on a standard dataset, our approach demonstrates significant enhancements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to conventional methods.