Li, JizhouJizhouLiXue, FengFengXueTHIERRY BLU2024-03-072024-03-072017-07-02978150902175815224880https://scholars.lib.ntu.edu.tw/handle/123456789/640488Blur estimation is critical to blind image deconvolution. In this work, by taking Gaussian kernel as an example, we propose an approach to estimate the blur size for photon-limited images. This estimation is based on the minimization of a novel criterion, blur-PURE (Poisson unbiased risk estimate), which makes use of the Poisson noise statistics of the measurement. Experimental results demonstrate the effectiveness of the proposed method in various scenarios. This approach can be then plugged into our recent PURE-LET deconvolution algorithm, and an example on real fluorescence microscopy is presented.Image deconvolution | Parametric blur estimation | Photon-limited images | Poisson noiseconference paper10.1109/ICIP.2017.82963302-s2.0-85045343267https://api.elsevier.com/content/abstract/scopus_id/85045343267