9,437 research outputs found
Fast O(1) bilateral filtering using trigonometric range kernels
It is well-known that spatial averaging can be realized (in space or
frequency domain) using algorithms whose complexity does not depend on the size
or shape of the filter. These fast algorithms are generally referred to as
constant-time or O(1) algorithms in the image processing literature. Along with
the spatial filter, the edge-preserving bilateral filter [Tomasi1998] involves
an additional range kernel. This is used to restrict the averaging to those
neighborhood pixels whose intensity are similar or close to that of the pixel
of interest. The range kernel operates by acting on the pixel intensities. This
makes the averaging process non-linear and computationally intensive,
especially when the spatial filter is large. In this paper, we show how the
O(1) averaging algorithms can be leveraged for realizing the bilateral filter
in constant-time, by using trigonometric range kernels. This is done by
generalizing the idea in [Porikli2008] of using polynomial range kernels. The
class of trigonometric kernels turns out to be sufficiently rich, allowing for
the approximation of the standard Gaussian bilateral filter. The attractive
feature of our approach is that, for a fixed number of terms, the quality of
approximation achieved using trigonometric kernels is much superior to that
obtained in [Porikli2008] using polynomials.Comment: Accepted in IEEE Transactions on Image Processing. Also see addendum:
https://sites.google.com/site/kunalspage/home/Addendum.pd
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
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