InpDiffusion treats image inpainting localization as a conditional mask-generation task with a diffusion model, using edge supervision to refine boundaries, and reports state-of-the-art AUC on Inpaint32K, DID, AutoSplice, IMD, and NIST.
S3RP: Self-Supervised Super-Resolution and Prediction for Advection-Diffusion Process
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abstract
We present a super-resolution model for an advection-diffusion process with limited information. While most of the super-resolution models assume high-resolution (HR) ground-truth data in the training, in many cases such HR dataset is not readily accessible. Here, we show that a Recurrent Convolutional Network trained with physics-based regularizations is able to reconstruct the HR information without having the HR ground-truth data. Moreover, considering the ill-posed nature of a super-resolution problem, we employ the Recurrent Wasserstein Autoencoder to model the uncertainty.
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InpDiffusion: Image Inpainting Localization via Conditional Diffusion Models
InpDiffusion treats image inpainting localization as a conditional mask-generation task with a diffusion model, using edge supervision to refine boundaries, and reports state-of-the-art AUC on Inpaint32K, DID, AutoSplice, IMD, and NIST.