Block adaptive compressed sensing of SAR images based on statistical character

Abstract

Block-based processing has shown promise to reduce computation complexity and storage space for image Compressed Sensing. In this paper, a new architecture for SAR images is proposed, as an improvement for traditional Block Compressed Sensing of natural images. The proposed scheme adopts the basic structure of existing Block Compressed Sensing, and studies the character of SAR images. Based on the difference of statistical property among sub blocks, the proposed scheme can adaptively select the number of measurements that needed to take for every sub blocks. Different from equality measurement, adaptive sampling can sufficiently capture the diversity between sub blocks and keep their properties well. Several numeral experiments also demonstrate that the proposed approach outperforms the existing scheme, achieving comparable reconstruction quality via fewer measurements.

Drone SAR Image Compression Based on Block Adaptive Compressive Sensing

“…In this subsection, we introduce an adaptive BCS method in which an individual block has a different measurement ratio in the range of 0 < r ≤ 1 determined by several criteria such as the variance, the equivalent noise level, and the local salient factor in each block [20,21,34]. For example, when the measurement ratio is determined according to the variance of each image block, the number of measurements for the ith block is computed as…”

“…Whereas this projection process is performed in the matrix form as in (7) and (12) for the BCS-SPL with a fixed measurement ratio, the projected vector is separately evaluated using a different Φ B,i as in ( 16) and ( 17) for the BCS-SPL with fully adaptive measurement ratios. Therefore, the adaptive BCS methods in [20,21,34] increase the compression efficiency by allowing an arbitrary measurement ratio over (0, 1], however the amount of side information is very large and the computational load for image reconstruction is very high due to the increased number of measurement matrices. Note that the cardinality of {Φ B,i } is N B in the worst case.…”

“…The CS theory has been successfully addressed in raw data compression of synthetic aperture radar (SAR) [22][23][24], high-resolution SAR image formation [25][26][27][28][29][30][31], SAR imaging with motion compensation [32,33], and compression of SAR images [34,35]. Moreover, the CS-SAR imaging schemes have been verified through hardware implementations and field experiments [36][37][38][39].…”

“…The compression efficiency is further enhanced by employing quadtree coding in the DWT domain [48] or preserving the wavelet coefficients for low frequency subbands while sparsely representing the coefficients for high frequency subbands using dictionaries [49]. In addition, the BCS was applied to compression of SAR images utilizing statistical character and blockwise CS recovery [34].…”

“…Fully adaptive BCS: the method in [34] is used. Block measurement ratios are assigned according to block variances in the image-domain, and SAR images are reconstructed by separately applying the BCS-SPL to each block as explained in Section 2.4.…”