The content discusses the challenges of blind image restoration, introduces a Bayesian generative model, and presents an inference algorithm. It compares different methodologies and evaluates the proposed method's performance in denoising and super-resolution tasks.
Classical model-based methods focus on image priors, while DL-based methods use DNNs for direct learning. The proposed method combines advantages from both approaches. Experiments show superior performance over state-of-the-art methods.
The paper details the formulation of variational posterior distributions, evidence lower bound derivation, network structures, and training strategies. Results demonstrate the effectiveness of the proposed VIRNet in handling non-i.i.d. noise configurations.
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