Scale Adaptive Blind Deblurring
The presence of noise and small scale structures usually leads to large kernel estimation 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 suggests 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 effectiveness of the proposed method. One surprising finding based on our approach is that blur kernel estimation is not necessarily best at the finest scale.
The paper starts from the hypothesis that image structures that are “small scale” or with gradient profiles distributed near-each other are invariably recovered incorrectly when the size of the blur kernel is similar to their size. A consequence of this is poor kernel estimatesintentionally blurring as done in a coarse-to-fine deblurring process naturally suppresses the contributions of these small scale structures since their contributions is downplayed at coarser levels.Building on these ideas, the paper suggests a joint blink deblurring across the different scales with the constraint that the latent image remains the same for all scales.Parameters are tuned/learnt to account for different levels of noise Pros- Novel idea of jointly deblurring across multiple scales with constraints.- Improvement over competing methods.Cons- Claims of newer analysis not supported or overstated (?)- Details on non-uniform blur models missing1) The idea of jointly deblurring across multi-scales, to the best of my knowledge, is new.it raises some question though.First, wouldn’t such a method over penalize errors in the coarser scales — since they are present in all the scales ? Second, the paper suggests the finer scales are less robust to noise and while coarser scales are robust to noise, they lack fine detail.While i agree with this motivation, does the proposed method make better use of it than other techniques — say Fergus et al. where a coarse to fine refinement is performed under the same motivation.2) The paper keeps suggesting that while similar techniques have been proposed, the  paper provides motivation and analysis.I am not sure what analysis the paper refers to.There are experimental results that suggests that the  joint multi-scale deblurring has benefits. A lot of claims are made in lines 262-294. But these are largely empirical and many details are missing. What was the sample size with respect to number of latent images, number of blur kernels,  and hence, wouldnt quite pass for a rigorous analysis.3) The paper could do a better job in describing the non-uniform model since they are used in the real examples. Specifically, reporting how the homographs were generated and the number used etc. should help reproducibility of the results.Smaller comments- As opposed to “Estimation error” which is very hard to interpret, the paper should report scores with SNR or PSNR. There are normalized and in log-scale — both of which make it significantly easier to interpret.- Line 283. “This corroborated previous findings” — citations are required here.- Figure 6. Pl show recovered blur kernels for all images. These are valuable in interpreting the results.