366 research outputs found
Artifact reduction for separable non-local means
It was recently demonstrated [J. Electron. Imaging, 25(2), 2016] that one can
perform fast non-local means (NLM) denoising of one-dimensional signals using a
method called lifting. The cost of lifting is independent of the patch length,
which dramatically reduces the run-time for large patches. Unfortunately, it is
difficult to directly extend lifting for non-local means denoising of images.
To bypass this, the authors proposed a separable approximation in which the
image rows and columns are filtered using lifting. The overall algorithm is
significantly faster than NLM, and the results are comparable in terms of PSNR.
However, the separable processing often produces vertical and horizontal
stripes in the image. This problem was previously addressed by using a
bilateral filter-based post-smoothing, which was effective in removing some of
the stripes. In this letter, we demonstrate that stripes can be mitigated in
the first place simply by involving the neighboring rows (or columns) in the
filtering. In other words, we use a two-dimensional search (similar to NLM),
while still using one-dimensional patches (as in the previous proposal). The
novelty is in the observation that one can use lifting for performing
two-dimensional searches. The proposed approach produces artifact-free images,
whose quality and PSNR are comparable to NLM, while being significantly faster.Comment: To appear in Journal of Electronic Imagin
Fast Separable Non-Local Means
We propose a simple and fast algorithm called PatchLift for computing
distances between patches (contiguous block of samples) extracted from a given
one-dimensional signal. PatchLift is based on the observation that the patch
distances can be efficiently computed from a matrix that is derived from the
one-dimensional signal using lifting; importantly, the number of operations
required to compute the patch distances using this approach does not scale with
the patch length. We next demonstrate how PatchLift can be used for patch-based
denoising of images corrupted with Gaussian noise. In particular, we propose a
separable formulation of the classical Non-Local Means (NLM) algorithm that can
be implemented using PatchLift. We demonstrate that the PatchLift-based
implementation of separable NLM is few orders faster than standard NLM, and is
competitive with existing fast implementations of NLM. Moreover, its denoising
performance is shown to be consistently superior to that of NLM and some of its
variants, both in terms of PSNR/SSIM and visual quality
Mixture of multiple copies of maximally entangled states is quasi-pure
Employing the general BXOR operation and local state discrimination, the
mixed state of the form
\rho^{(k)}_{d}=\frac{1}{d^{2}}\sum_{m,n=0}^{d-1}(|\phi_{mn}><\phi_{mn}|)^{\otim
es k} is proved to be quasi-pure, where is the canonical set
of mutually orthogonal maximally entangled states in . Therefore
irreversibility does not occur in the process of distillation for this family
of states. Also, the distillable entanglement is calculated explicitly.Comment: 6 pages, 1 figure. The paper is subtantially revised and the general
proof is give
Correlation approach to work extraction from finite quantum systems
Reversible work extraction from identical quantum systems via collective
operations was shown to be possible even without producing entanglement among
the sub-parts. Here, we show that implementing such global operations
necessarily imply the creation of quantum correlations, as measured by quantum
discord. We also reanalyze the conditions under which global transformations
outperform local gates as far as maximal work extraction is considered by
deriving a necessary and sufficient condition that is based on classical
correlations
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
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