11,254 research outputs found
On a fast bilateral filtering formulation using functional rearrangements
We introduce an exact reformulation of a broad class of neighborhood filters,
among which the bilateral filters, in terms of two functional rearrangements:
the decreasing and the relative rearrangements.
Independently of the image spatial dimension (one-dimensional signal, image,
volume of images, etc.), we reformulate these filters as integral operators
defined in a one-dimensional space corresponding to the level sets measures.
We prove the equivalence between the usual pixel-based version and the
rearranged version of the filter. When restricted to the discrete setting, our
reformulation of bilateral filters extends previous results for the so-called
fast bilateral filtering. We, in addition, prove that the solution of the
discrete setting, understood as constant-wise interpolators, converges to the
solution of the continuous setting.
Finally, we numerically illustrate computational aspects concerning quality
approximation and execution time provided by the rearranged formulation.Comment: 29 pages, Journal of Mathematical Imaging and Vision, 2015. arXiv
admin note: substantial text overlap with arXiv:1406.712
Reflectance Adaptive Filtering Improves Intrinsic Image Estimation
Separating an image into reflectance and shading layers poses a challenge for
learning approaches because no large corpus of precise and realistic ground
truth decompositions exists. The Intrinsic Images in the Wild~(IIW) dataset
provides a sparse set of relative human reflectance judgments, which serves as
a standard benchmark for intrinsic images. A number of methods use IIW to learn
statistical dependencies between the images and their reflectance layer.
Although learning plays an important role for high performance, we show that a
standard signal processing technique achieves performance on par with current
state-of-the-art. We propose a loss function for CNN learning of dense
reflectance predictions. Our results show a simple pixel-wise decision, without
any context or prior knowledge, is sufficient to provide a strong baseline on
IIW. This sets a competitive baseline which only two other approaches surpass.
We then develop a joint bilateral filtering method that implements strong prior
knowledge about reflectance constancy. This filtering operation can be applied
to any intrinsic image algorithm and we improve several previous results
achieving a new state-of-the-art on IIW. Our findings suggest that the effect
of learning-based approaches may have been over-estimated so far. Explicit
prior knowledge is still at least as important to obtain high performance in
intrinsic image decompositions.Comment: CVPR 201
A multiresolution framework for local similarity based image denoising
In this paper, we present a generic framework for denoising of images corrupted with additive white Gaussian noise based on the idea of regional similarity. The proposed framework employs a similarity function using the distance between pixels in a multidimensional feature space, whereby multiple feature maps describing various local regional characteristics can be utilized, giving higher weight to pixels having similar regional characteristics. An extension of the proposed framework into a multiresolution setting using wavelets and scale space is presented. It is shown that the resulting multiresolution multilateral (MRM) filtering algorithm not only eliminates the coarse-grain noise but can also faithfully reconstruct anisotropic features, particularly in the presence of high levels of noise
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