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.

Recommended citation: Abdul Mohaimen Al Radi, Prothito Shovon Majumder, & Md. Mosaddek Khan. (2024). Blind Image Deblurring with FFT-ReLU Sparsity Prior.
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