27 February 2015 Real-time deblurring of handshake blurred images on smartphones
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This paper discusses an Android app for the purpose of removing blur that is introduced as a result of handshakes when taking images via a smartphone. This algorithm utilizes two images to achieve deblurring in a computationally efficient manner without suffering from artifacts associated with deconvolution deblurring algorithms. The first image is the normal or auto-exposure image and the second image is a short-exposure image that is automatically captured immediately before or after the auto-exposure image is taken. A low rank approximation image is obtained by applying singular value decomposition to the auto-exposure image which may appear blurred due to handshakes. This approximation image does not suffer from blurring while incorporating the image brightness and contrast information. The eigenvalues extracted from the low rank approximation image are then combined with those from the shortexposure image. It is shown that this deblurring app is computationally more efficient than the adaptive tonal correction algorithm which was previously developed for the same purpose.
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Reza Pourreza-Shahri, Reza Pourreza-Shahri, Chih-Hsiang Chang, Chih-Hsiang Chang, Nasser Kehtarnavaz, Nasser Kehtarnavaz, "Real-time deblurring of handshake blurred images on smartphones", Proc. SPIE 9400, Real-Time Image and Video Processing 2015, 940008 (27 February 2015); doi: 10.1117/12.2077219; https://doi.org/10.1117/12.2077219

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