With the development of multi-pass synthetic aperture radar (SAR) imaging, feature enhancement with multiple SAR images has become an important research topic due to its distinct advantage of redundant scene information. The existing SAR image feature enhancement algorithms mainly focus on single SAR image processing by means of ℓq(02,1-norm minimization approach is introduced to achieve the purpose of feature enhancement. In addition, two dimensional fast iterative shrinkage thresholding algorithm (2D-FISTA) is utilized to increase the computational efficiency. Experimental results are illustrated to validate that the proposed method can improve SAR image performance significantly in terms of denoising and sidelobes suppression while maintaining higher computational efficiency.

Airborne multiple-input multiple-output array synthetic aperture radar (MIMO array SAR) can be used to directly obtain the three-dimensional (3-D) imagery of the illuminated region with a single track. Different sparse reconstruction algorithms within the framework of compressive sensing (CS) have been conceived to reconstruct the cross-track signal because of its inherent spatial sparsity. However, the computational complexity and the number of two-dimensional (2-D) SAR images needed for sparse reconstruction of the existing algorithms are usually high. To overcome this problem, the same range line cells based 2-D real-valued CS is proposed in this paper. In this new algorithm, the original signal model is transformed from the complex domain to the real domain by means of unitary transformation. Finally, airborne MIMO array SAR real experimental results are illustrated to validate the prominent advantages of the proposed method.

Multiple-input multiple-output (MIMO) array synthetic aperture radar (SAR) with array antennas distributed along the cross-track direction can obtain 3-D scene information of the surveillance region. However, the cross-track resolution is unacceptable due to the length limitation of the MIMO antenna array. The superresolution algorithms within the framework of compressive sensing (CS) have been introduced to recover the cross-track signal because of its inherent spatial sparsity. The existing sparse recovery algorithms for 3-D SAR are attempted to find the sparse solution in the complex domain directly, which requires a very high computational complexity. To overcome this problem, a new fast 3-D imaging algorithm based on real-valued sparse representation is proposed in this paper. In this new algorithm, unitary transformation can be employed to transform the sparse signal recovery model of uniform/nonuniform MIMO array SAR from the complex domain to the real domain. Thus, a real-valued reweighted 12,1-norm minimization model is established. In addition, a modification of the fast iterative shrinkage-thresholding algorithm (FISTA) is used to reconstruct the 3-D image for further improving the computational efficiency. Moreover, the theoretical analysis of computational complexity of the proposed algorithm is derived when compared with an existing complex domain algorithm. Finally, numerical simulations and MIMO array SAR real experimental results are illustrated to validate that the proposed algorithm can reduce the computational complexity significantly in terms of CPU time while still maintaining the inherent advantages of superresolution and robustness against the noise.

MOTIVATION: Comrad is a novel algorithmic framework for the integrated analysis of RNA-Seq and whole genome shotgun sequencing (WGSS) data for the purposes of discovering genomic rearrangements and aberrant transcripts. The Comrad framework leverages the advantages of both RNA-Seq and WGSS data, providing accurate classification of rearrangements as expressed or not expressed and accurate classification of the genomic or non-genomic origin of aberrant transcripts. A major benefit of Comrad is its ability to accurately identify aberrant transcripts and associated rearrangements using low coverage genome data. As a result, a Comrad analysis can be performed at a cost comparable to that of two RNA-Seq experiments, significantly lower than an analysis requiring high coverage genome data. RESULTS: We have applied Comrad to the discovery of gene fusions and read-throughs in prostate cancer cell line C4-2, a derivative of the LNCaP cell line with androgen-independent characteristics. As a proof of concept, we have rediscovered in the C4-2 data 4 of the 6 fusions previously identified in LNCaP. We also identified six novel fusion transcripts and associated genomic breakpoints, and verified their existence in LNCaP, suggesting that Comrad may be more sensitive than previous methods that have been applied to fusion discovery in LNCaP. We show that many of the gene fusions discovered using Comrad would be difficult to identify using currently available techniques. AVAILABILITY: A C++ and Perl implementation of the method demonstrated in this article is available at http://compbio.cs.sfu.ca/.