Deep Autoencoder-Based Image Reconstruction and Denoising for Enhanced Digital Image Matching

Image denoising plays a crucial role in computer vision by improving image quality and facilitating subsequent image processing and recognition tasks. This paper presents an autoencoder-based neural network for image reconstruction and noise removal. The proposed method is evaluated using handwritten digit images from the MNIST dataset corrupted with Gaussian and salt-and-pepper noise. These noise types can distort important structural features of handwritten digits and negatively affect recognition performance. The autoencoder learns to reconstruct clean images from their noisy counterparts, suppressing unwanted noise while preserving essential visual and structural information. The reconstructed images demonstrate improved clarity and preservation of digit shapes, particularly when handwriting is faint, discontinuous, or partially degraded. The proposed framework may also support handwritten text recognition and image matching by maintaining structural characteristics that are important for reliable recognition. Experimental results show that the proposed method achieves mean squared error (MSE) values of 0.0148 for Gaussian noise and 0.0117 for salt-and-pepper noise, demonstrating its ability to reconstruct noisy handwritten digit images with low reconstruction error.