5,298 research outputs found
Edge Guided Reconstruction for Compressive Imaging
We propose EdgeCS—an edge guided compressive sensing reconstruction approach—to recover images
of higher quality from fewer measurements than the current methods. Edges are important
image features that are used in various ways in image recovery, analysis, and understanding. In
compressive sensing, the sparsity of image edges has been successfully utilized to recover images.
However, edge detectors have not been used on compressive sensing measurements to improve the
edge recovery and subsequently the image recovery. This motivates us to propose EdgeCS, which
alternatively performs edge detection and image reconstruction in a mutually beneficial way. The
edge detector of EdgeCS is designed to faithfully return partial edges from intermediate image reconstructions
even though these reconstructions may still have noise and artifacts. For complex-valued
images, it incorporates joint sparsity between the real and imaginary components. EdgeCS has
been implemented with both isotropic and anisotropic discretizations of total variation and tested
on incomplete k-space (spectral Fourier) samples. It applies to other types of measurements as well.
Experimental results on large-scale real/complex-valued phantom and magnetic resonance (MR)
images show that EdgeCS is fast and returns high-quality images. For example, it exactly recovers
the 256×256 Shepp–Logan phantom from merely 7 radial lines (3.03% k-space), which is impossible
for most existing algorithms. It is able to accurately reconstruct a 512 Ă— 512 MR image with 0.05
white noise from 20.87% radial samples. On complex-valued MR images, it obtains recoveries with
faithful phases, which are important in many medical applications. Each of these tests took around
30 seconds on a standard PC. Finally, the algorithm is GPU friendly
Signal reconstruction via operator guiding
Signal reconstruction from a sample using an orthogonal projector onto a
guiding subspace is theoretically well justified, but may be difficult to
practically implement. We propose more general guiding operators, which
increase signal components in the guiding subspace relative to those in a
complementary subspace, e.g., iterative low-pass edge-preserving filters for
super-resolution of images. Two examples of super-resolution illustrate our
technology: a no-flash RGB photo guided using a high resolution flash RGB
photo, and a depth image guided using a high resolution RGB photo.Comment: 5 pages, 8 figures. To appear in Proceedings of SampTA 2017: Sampling
Theory and Applications, 12th International Conference, July 3-7, 2017,
Tallinn, Estoni
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