Motion Deblurring Through Autoencoder-Based Image Restoration

The quality of the image is very important in image processing. Image blurring is a most common issue, which caused to reduce the quality of the image. Blurred images often occur due to camera shake, object motion, or long exposure times, leading to a loss of critical details and visual fidelity. Deblurring of an image is a challenging task, and there are very few researches available in this field. This proposed study is focusing on introducing a novel approach to detect and deblur motion-blurred images using autoencoder mechanism. This proposed study will be a remarkable era in image quality restoration in various real-world applications, such as surveillance, medical imaging, autonomous vehicles, and photography. The inability to recover sharp images from blurred ones can significantly impact the accuracy and reliability of image-based systems in these sectors. The primary objective of this research is to develop an advanced and practical solution to restore motion-blurred images with high accuracy and speed, enabling improved decision-making and analysis in critical domains. The motion deblurring model makes use of an autoencoder architecture, which includes an encoder and a decoder. The encoder reduces spatial dimensions gradually, forming a condensed representation in latent space. To generate deblurred images, the decoder, its mirror counterpart, upsamples the latent space using transposed convolutional layers. Over 100 epochs, the end-to-end autoencoder learns the difficult mapping between motion-blurred and clear images, attaining over 80% accuracy.