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AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusion
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We present AutoDIR, an innovative all-in-one image restoration system incorporating latent diffusion. AutoDIR excels in its ability to automatically identify and restore images suffering from a range of unknown degradations. AutoDIR offers intuitive open-vocabulary image editing, empowering users to customize and enhance images according to their preferences. Specifically, AutoDIR consists of two key stages: a Blind Image Quality Assessment (BIQA) stage based on a semantic-agnostic vision-language model which automatically detects unknown image degradations for input images, an All-in-One Image Restoration (AIR) stage utilizes structural-corrected latent diffusion which handles multiple types of image degradations. Extensive experimental evaluation demonstrates that AutoDIR outperforms state-of-the-art approaches for a wider range of image restoration tasks. The design of AutoDIR also enables flexible user control (via text prompt) and generalization to new tasks as a foundation model of image restoration. Project is available at: \url{https://jiangyitong.github.io/AutoDIR_webpage/}.
Forward citations
Cited by 4 Pith papers
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GenDeg: Diffusion-based Degradation Synthesis for Generalizable All-In-One Image Restoration
A controlled diffusion model, GenDeg, generates a large dataset of diverse paired degradations that improves out-of-distribution performance of all-in-one image restoration models when used as training data.
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Complexity Experts are Task-Discriminative Learners for Any Image Restoration
MoCE-IR uses mixture-of-experts layers where experts have different complexity and a complexity-biased router assigns tasks to the right expert, improving all-in-one image restoration.
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DR-BFR: Degradation Representation with Diffusion Models for Blind Face Restoration
DR-BFR learns a content-free degradation representation from low-quality faces and uses it as a prompt to condition a latent diffusion face restoration model, improving FID and NIQE on face benchmarks.
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DiffIER: Optimizing Diffusion Models with Iterative Error Reduction
DiffIER claims that iteratively minimizing the distance between a diffusion model's predicted noise and a random Gaussian sample at each inference step improves generation quality.
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