Scale Adaptive Blind Deblurring
Haichao Zhang, Shuicheng Yan · 2014
The presence of noise and small scale structures usually leads to large kernel es-timation errors in blind image deblurring empirically, if not a total failure. We present a scale space perspective on blind deblurring algorithms, and introduce a cascaded scale space formulation for blind deblurring. This new formulation sug-gests a natural approach robust to noise and small scale structures through tying the estimation across multiple scales and balancing the contributions of different scales automatically by learning from data. The proposed formulation also allows to handle non-uniform blur with a straightforward extension. Experiments are conducted on both benchmark dataset and real-world images to validate the effec-tiveness of the proposed method. One surprising finding based on our approach is that blur kernel estimation is not necessarily best at the finest scale. 1