
Submitted by
Assigned_Reviewer_4
Q1: Comments to author(s).
First provide a summary of the paper, and then address the following
criteria: Quality, clarity, originality and significance. (For detailed
reviewing guidelines, see
http://nips.cc/PaperInformation/ReviewerInstructions)
In this paper, the authors develop a novel blind
deconvolution algorithm that provides very good results for both uniform
and nonuniform blind deconvolution problems. They provide a compelling
analysis of the reasons why their method works well. A number of
experimental results are provided against the stateofthe art algorithms
for both uniform and nonuniform cases. The paper is clear and elegantly
written. I believe this is a significant contribution to the literature on
blind deconvolution.
It would be interesting if the authors
discussed how their new technique relates to the variational deblurring
algorithm of Levin et. al. [15], which also uses marginalization over x to
determine the blur kernel. In fact, the authors' new formulation starts
with an even more simplified prior than the heavy tailed one used by Levin
et. al. However, instead of fixed variances as used in Levin et. al., the
authors make these variances spatially varying hyperparameters...is this
the crucial difference? Q2: Please summarize your review
in 12 sentences
A novel and interesting new algorithm for blind
deconvolution is presented. Submitted by
Assigned_Reviewer_5
Q1: Comments to author(s).
First provide a summary of the paper, and then address the following
criteria: Quality, clarity, originality and significance. (For detailed
reviewing guidelines, see
http://nips.cc/PaperInformation/ReviewerInstructions)
This paper describes a method for nonuniform
(spacevarying) blind deconvolution. The method is strongly based on
ref [26]. Indeed the authors propose to solve eq. 9, which is
essentially the same as in eq. 26 and 27 in ref [26], by using the
upper bound derived in eq 7, which is one of the novel bits. The
analysis of the effects of such prior, however, is carried out on the
original cost. A summary of the analysis is that the proposed prior
adaptively regulates the concavity/convexity of the imageblur prior
depending on the magnitude of the local image gradients and the L2
norm of the blur. If the blur is given at any pixel, the theory is
that a high L2 norm (up to 1) can only be achieved with a Dirac delta
and hence the image is sharp to start with. Here a very concave prior
which strongly induces gradient sparsity is welcome. Vice versa, where
the blur tends to be an averaging kernel, the prior tends to be much
less concave to allow for less sensitivity to fine details and more
sensitivity to coarse details.
Quality  The
algorithm and the underlining theory are interesting and compelling.
Although a good portion of the paper is devoted to explaining the
effect of the proposed prior, there are several points left
unexamined. 1  The analysis is carried out on the original cost 9 but
the effects of the bound 7, which is the novelty in the algorithm, are
not discussed. 2 It would be useful to see how the analysis changes
under the uniform case (that would complement well with the
experiments in Fig 2). 3 In light of the results in ref [16] it would
be useful to see a discussion of how this prior addresses the
limitations of the classic priors; specifically, it would be
interesting to see that the blurry image & no blur solution is no
longer a global minimum, or, even better, no longer a local minimum.
Clarity  Overall the authors do a good job with
explaining the approach. However, there is somehow a jump between the
paragraphs in sec 3. For example, the connection between eq 7 (the
approx) and cost 9 is not made clear. Indeed eq. 7 is never used later
on. One can then find it only in the supplementary material. I
recommend to revise/rewrite this section. The use of w for the weights
and \hat w for the blur kernels is quite confusing. Despite the
relation in eq 8, their meaning is very different. Please consider
changing one of the two (e.g. h_i for \hat w_i would be much more
meaningful). Eq 6 might have some typos: check that T at the exponent
is not 1 and that you are not missing a product over i under the
integral.
Originality  The originality is
limited by all the body of work by ref [26]. It probably would have
been very useful to discuss the differences with respect to [26]. In
my opinion the originality is limited to a bound (eq7) and an
explanation of how this prior operates (via Theorem 1). The
explanation however, is quite approximate due to the complexity of the
prior.
Significance  The study and development of
novel priors for blind deconvolution is quite important and this paper
further develops the new direction introduced by ref [26]. Moreover,
given the experimental performance of this algorithm, this approach
deserves attention.
Q2: Please
summarize your review in 12 sentences
Overall this paper introduces some novel elements: a
practical bound for a cost function that simplifies the implementation,
and analysis that explains the general behavior of the adaptive prior.
The performance is quite good. Submitted by
Assigned_Reviewer_6
Q1: Comments to author(s).
First provide a summary of the paper, and then address the following
criteria: Quality, clarity, originality and significance. (For detailed
reviewing guidelines, see
http://nips.cc/PaperInformation/ReviewerInstructions)
The paper addresses the problem of single image blind
deconvolution with nonuniform blur caused by camera shake. The authors
propose a two step procedure: first to estimate the motion blur and second
the recovery of the soughtafter latent image through nonblind
deconvolution. This procedure is common practice in blind deconvolution.
The main contribution made here is the derivation of a Bayesian inference
strategy for motion blur estimation. In a number of experiments the
authors demonstrate the validity of their approach and compare its
performance against other stateoftheart methods. The mathematical
derivation appears sound and the proposed scheme is claimed to exhibit a
number of favourable properties. Although these details are discussed in
some detail in the second half of Section 4, no empirical evidence is
given, which would strengthen the argumentation and verify the claims
made. The authors also miss to make connections to other recent approaches
that advocate a similar line of reasoning, in particular [15] and the
missing reference:
S.D.Babacan, et al., "Bayesian Blind
Deconvolution with General Sparse Image Priors", ECCV 2012
In
addition, the authors miss another relevant reference, namely
R.Kohler, et al., "Recording and playback of camera shake:
Benchmarking blind deconvolution with a realworld database." ECCV 2012.
that would allow them to benchmark their approach against other
stateoftheart single image blind deblurring methods for both uniform
and nonuniform blur. Besides, a number of questions remain unanswered
(see below) in the presented exposition, that lower the overall quality of
the paper.
The paper is clearly structured and well written.
Section 4 would benefit from a division in smaller subsections as it feels
a bit lengthy for my tasting. Unfortunately, some important details are
missing (e.g. which algorithm is used for the final nonblind
deconvolution, see also comments below), which impair the overall clarity
of the paper.
The theoretical foundation of the proposed approach
build on previous work [19,26], however its application to the problem of
single image blind deconvolution seems novel.
Although the
presented algorithm seems to deliver comparable results to
stateoftheart algorithms, the provided insights are very limited. It
remains unclear in which situations/cases the proposed method works better
and why. It also fails to make connections to other recent relevant work
(see above) and place its contribution into context, which lowers its
significance considerably.
Further comments: * What method is
employed for the final nonblind deconvolution? * It is stated, that
the approach is "parameterfree". Is this also true for the kernel size?
* It is mentioned in Section 4, that initialization with a large
$\lambda$ and its subsequent evolution render the deconvolution process
more stable. It would be interesting to see a plot that shows the
evolution of $\lambda$ in the course of the motion blur estimation
process. * What basis (i.e. $B$ in Eq.(8)) is chosen for the
experiments with nonuniform blur? Is $w^\bar_i$ evaluated for every
pixel? If not, how are the evaluation locations chosen? * From the
main paper it is not clear that a coarsetofine scheme is employed. Great
if this could be made clearer. * What are typical runtimes for the
proposed method? * Large blur is challenging even for stateoftheart
methods. How does performance scale with kernels size? * The ringing
artifacts in Whyte's result in Fig.4 are stemming primarily from the final
nonblind deconvolution (see his talk slides). It would be more fair to
use the one published on the accompanying project webpage
(http://www.di.ens.fr/willow/research/deblurring/). * The correct
reference for the efficient filter flow framework mentioned in Section 2
is M.Hirsch, et al., "Efficient filter flow for spacevariant
multiframe blind deconvolution." CVPR 2010. * What are the limitations
of the proposed approach?
Q2: Please summarize your
review in 12 sentences
The paper presents an interesting Bayesian approach to
motion blur estimation. Unfortunately, it fails to make connections to
relevant previous work and lacks a quantitative evaluation for the case of
camera shake removal with nonuniform blur, which lower its significance
and value to the community considerably.
Q1:Author
rebuttal: Please respond to any concerns raised in the reviews. There are
no constraints on how you want to argue your case, except for the fact
that your text should be limited to a maximum of 6000 characters. Note
however that reviewers and area chairs are very busy and may not read long
vague rebuttals. It is in your own interest to be concise and to the
point.
The revised version of our paper and the associated
supplementary material address the major concerns of the reviewers and
clarify other points and analyses that were previously unclear.
 