Lucky DCT aggregation for camera shake removal

• Published in: 2017 IEEE International Conference on Image Processing (ICIP) pp. 3790 - 3794
• Main Authors: Ghosh, Sanjay, Naik, Satyajit, Chaudhury, Kunal N.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2017
• Subjects: Burst imaging; camera shake removal; Cameras; discrete cosine transform; Discrete cosine transforms; Kernel; lucky imaging; Noise level; Photography; Robustness
• ISSN: 2381-8549
• DOI: 10.1109/ICIP.2017.8296991

We consider the task of removing the effect of camera shake during a long exposure. Technically, this is a blind deconvolution problem in which both the image and the motion blur have to be jointly inferred. Several algorithms have been proposed till date for removing camera shake that work with one or more images. However, most of these algorithms are computationally expensive and hence cannot be used in real-time. In this work, we propose a simple and cheap algorithm that can effectively recover the original sharp image from multiple burst images (captured using the burst modality of modern cameras). In summary, we pick selected images from the burst (using ideas from lucky imaging), which are then aggregated using the discrete cosine transform (similar to the idea of Fourier burst accumulation). We present some preliminary results and comparisons to demonstrate the effectiveness of the proposal.