High-resolution image inpainting using a probabilistic framework for diverse images with large arbitrary masks

Addressing inpainting challenges in high-resolution images remains a complex task. The most recent image inpainting techniques rely on machine learning models; however, a major limitation of supervised methods is their dependence on end-to-end training. Even minor changes to the input often necessitate retraining, making the process inefficient. As a result, unsupervised learning approaches have gained prominence in image inpainting. State-of-the-art methods, particularly those using generative adversarial networks (GANs), have achieved promising results. However, generating photorealistic outputs for high-resolution images with arbitrary large-region masks remains difficult. Inpainted images often suffer from deformed structures and blurry textures, compromising quality. Additionally, building a model capable of handling a diverse range of images presents further challenges. These challenges are addressed by proposing a novel probabilistic model that utilizes picture priors to learn prominent features within StyleGAN3. The priors are constructed using cosine similarity, mean, and intensity, where intensity is computed using the improved Papoulis-Gerchberg algorithm. The image is reconstructed using the probabilistic maximum a posteriori estimate. Variational inference is then applied to obtain the optimal solution using a modified Bayes-by-Backprop approach. The model is evaluated on 70,000 images from the Flickr-Faces-HQ, DIV2K, and brain datasets and surpasses state-of-the-art techniques in reconstruction quality.