857 research outputs found
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
A general framework for solving image inverse problems is introduced in this
paper. The approach is based on Gaussian mixture models, estimated via a
computationally efficient MAP-EM algorithm. A dual mathematical interpretation
of the proposed framework with structured sparse estimation is described, which
shows that the resulting piecewise linear estimate stabilizes the estimation
when compared to traditional sparse inverse problem techniques. This
interpretation also suggests an effective dictionary motivated initialization
for the MAP-EM algorithm. We demonstrate that in a number of image inverse
problems, including inpainting, zooming, and deblurring, the same algorithm
produces either equal, often significantly better, or very small margin worse
results than the best published ones, at a lower computational cost.Comment: 30 page
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
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
Depth Superresolution using Motion Adaptive Regularization
Spatial resolution of depth sensors is often significantly lower compared to
that of conventional optical cameras. Recent work has explored the idea of
improving the resolution of depth using higher resolution intensity as a side
information. In this paper, we demonstrate that further incorporating temporal
information in videos can significantly improve the results. In particular, we
propose a novel approach that improves depth resolution, exploiting the
space-time redundancy in the depth and intensity using motion-adaptive low-rank
regularization. Experiments confirm that the proposed approach substantially
improves the quality of the estimated high-resolution depth. Our approach can
be a first component in systems using vision techniques that rely on high
resolution depth information
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