Super-resolution image with unknown degradation by iterative kernel and estimate noise with IKR-Net
Super-resolution image with unknown degradation by iterative kernel and estimate noise with IKR-Net
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation
arXiv paper abstract https://arxiv.org/abs/2404.16564
arXiv PDF paper https://arxiv.org/pdf/2404.16564
Blind single image super-resolution (SISR) is a challenging task in image processing due to the ill-posed nature of the inverse problem.
... propose IKR-Net (Iterative Kernel Reconstruction Network) for blind SISR.
In the proposed approach, kernel and noise estimation and high-resolution image reconstruction are carried out iteratively using dedicated deep models.
The iterative refinement provides significant improvement in both the reconstructed image and the estimated blur kernel even for noisy inputs.
IKR-Net provides a generalized solution that can handle any type of blur and level of noise in the input low-resolution image.
IKR-Net achieves state-of-the-art results in blind SISR, especially for noisy images with motion blur.
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