Reminiscent Net: Conditional GAN-based Old Image De-Creasing

Documents are an essential source of valuable information and knowledge, and photographs are a great way of reminiscing old memories and past events. However, it becomes difficult to preserve the quality of such ancient documents and old photographs for an extremely long time, as these images usually get damaged or creased due to various extrinsic effects. Utilizing image editing software like Photoshop to manually reconstruct such old photographs and documents is a strenuous and an enduring process. This paper attempts to leverage the generative modeling capabilities of Conditional Generative Adversarial Networks by utilizing specialized architectures for the Generator and the Discriminator. The proposed Reminiscent Net has a U-Net-based Generator with numerous feature maps for complete information transfer with the incorporation of location and contextual details, and the absence of dense layers allows utilization of diverse sized images. Implementation of the PatchGAN-based Discriminator that penalizes the image at the scale of patches has been proposed. NADAM optimizer has been implemented to enable faster and better convergence of the loss function. The proposed method produces visually appealing de-creased images, and experiments indicate that the architecture performs better than various novel approaches, both qualitatively and quantitatively.