Photo-realistic image restoration algorithms
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Date
2025
Authors
Засядько, Матвiй
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Abstract
In this work, a new algorithm to reconstruct the facial images from degraded inputs is proposed with the visual high-definition reconstruction as its goal. The approach utilizes edge map information in a generative adversarial network (GAN) framework to be able to restore more delicate local structures and semantic content. The architecture is consisting of three parts: a DeblurEncoder which takes a blurred face image and its corresponding edge map, a Generator which recovers high resolution, and a Latent Encoder which supervises in latent space using the consistency loss terms. Training is performed end-toend all the while using a combined loss function that includes L1 loss, LPIPS perceptual loss, SSIM-based structural similarity loss, total variation loss, and a latent alignment term. Our approach was evaluated on the CelebABlur dataset and achieved comparable results in terms of numerical evaluation and visual quality. The study also compares with some recent state-of-the-art methods such as StyleGAN-based latent optimization, Posterior-Mean Rectified Flow and DiffIR. An advantages of this method are the combination of edgeinformation and latent-space constraints, which results in the improved quality of generated images, and that all three model components are trained simultaneously, what provides more consistent learning across the latent and pixel spaces enhancing both visual fidelity and structural coherence.
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Keywords
image restoration, edge maps, GAN, diffusion models, LPIPS, SSIM, CelebABlur, DeblurEncoder, Generator, latent supervision, bachelor`s thesis