Blind Image Deblurring with FFT-ReLU Sparsity Prior
<|reference_start|>Blind Image Deblurring with FFT-ReLU Sparsity Prior: Blind image deblurring is the process of recovering a sharp image from a blurred one without prior knowledge about the blur kernel. It is a small data problem, since the key challenge lies in estimating the unknown degrees of blur from a single image or limited data, instead of learning from large datasets. The solution depends heavily on developing algorithms that effectively model the image degradation process. We introduce a method that leverages a prior which targets the blur kernel to achieve effective deblurring across a wide range of image types. In our extensive empirical analysis, our algorithm achieves results that are competitive with the state-of-the-art blind image deblurring algorithms, and it offers up to two times faster inference, making it a highly efficient solution.<|reference_end|>
@article{radi2024blind,
    title={Blind Image Deblurring with FFT-ReLU Sparsity Prior},
    author={Abdul Mohaimen Al Radi, Prothito Shovon Majumder, Md. Mosaddek Khan},
    journal={arXiv preprint arXiv:2406.08344},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08344},
    primaryClass={cs.CV}
}