9,179 research outputs found
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
Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization
As a powerful statistical image modeling technique, sparse representation has
been successfully used in various image restoration applications. The success
of sparse representation owes to the development of l1-norm optimization
techniques, and the fact that natural images are intrinsically sparse in some
domain. The image restoration quality largely depends on whether the employed
sparse domain can represent well the underlying image. Considering that the
contents can vary significantly across different images or different patches in
a single image, we propose to learn various sets of bases from a pre-collected
dataset of example image patches, and then for a given patch to be processed,
one set of bases are adaptively selected to characterize the local sparse
domain. We further introduce two adaptive regularization terms into the sparse
representation framework. First, a set of autoregressive (AR) models are
learned from the dataset of example image patches. The best fitted AR models to
a given patch are adaptively selected to regularize the image local structures.
Second, the image non-local self-similarity is introduced as another
regularization term. In addition, the sparsity regularization parameter is
adaptively estimated for better image restoration performance. Extensive
experiments on image deblurring and super-resolution validate that by using
adaptive sparse domain selection and adaptive regularization, the proposed
method achieves much better results than many state-of-the-art algorithms in
terms of both PSNR and visual perception.Comment: 35 pages. This paper is under review in IEEE TI
Image reconstruction in optical interferometry: Benchmarking the regularization
With the advent of infrared long-baseline interferometers with more than two
telescopes, both the size and the completeness of interferometric data sets
have significantly increased, allowing images based on models with no a priori
assumptions to be reconstructed. Our main objective is to analyze the multiple
parameters of the image reconstruction process with particular attention to the
regularization term and the study of their behavior in different situations.
The secondary goal is to derive practical rules for the users. Using the
Multi-aperture image Reconstruction Algorithm (MiRA), we performed multiple
systematic tests, analyzing 11 regularization terms commonly used. The tests
are made on different astrophysical objects, different (u,v) plane coverages
and several signal-to-noise ratios to determine the minimal configuration
needed to reconstruct an image. We establish a methodology and we introduce the
mean-square errors (MSE) to discuss the results. From the ~24000 simulations
performed for the benchmarking of image reconstruction with MiRA, we are able
to classify the different regularizations in the context of the observations.
We find typical values of the regularization weight. A minimal (u,v) coverage
is required to reconstruct an acceptable image, whereas no limits are found for
the studied values of the signal-to-noise ratio. We also show that
super-resolution can be achieved with increasing performance with the (u,v)
coverage filling. Using image reconstruction with a sufficient (u,v) coverage
is shown to be reliable. The choice of the main parameters of the
reconstruction is tightly constrained. We recommend that efforts to develop
interferometric infrastructures should first concentrate on the number of
telescopes to combine, and secondly on improving the accuracy and sensitivity
of the arrays.Comment: 15 pages, 16 figures; accepted in A&
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