BCS-SPL—Block Compressed Sensing with Smooth Projected Landweber Reconstruction Sungkwang Mun and James E. Fowler About BCS-SPL BCS-SPL combines block-based compressed-sensing sampling (BCS) of an image with a smoothed projected-Landweber (SPL) iterative reconstruction. Sampling is driven by random matrices applied on a block-by-block basis, while the reconstruction is a variant of projected Landweber (PL) reconstruction (also known as iterative hard thresholding (IHT)) that incorporates a smoothing operation (Wiener filtering) intended to reducing blocking artifacts. In essence, this filtering operation imposes smoothness in addition to the sparsity inherent to PL. MC-BCS-SPL is a motion-compensated version of BCS-SPL employing frame-to-frame residual reconstruction for video. MS-BCS-SPL is a multiscale version of BCS-SPL that deploys BCS within the domain of a wavelet transform. MH-BCS-SPL is a multihypothesis version of BCS-SPL with reconstruction driven by the measurement-domain residual resulting from multiple predictions culled from neighboring blocks. BCS-SPL-DPCM applies DPCM and uniform scalar quantization to BCS-SPL to produce a compressed bitstream from the BCS measurements. MC-CS-PL is a motion-compensated compressed-sensing reconstruction for dynamic MRI (does not use BCS, but adapts PL reconstruction to dynamic MRI). Publications S. Mun and J. E. Fowler, “Block Compressed Sensing of Images Using Directional Transforms,” in Proceedings of the International Conference on Image Processing, Cairo, Egypt, November 2009, pp. 3021-3024. S. Mun and J. E. Fowler, “Residual Reconstruction for Block-Based Compressed Sensing of Video,” in Proceedings of the IEEE Data Compression Conference, J. A. Storer and M. W. Marcellin, Eds., Snowbird, UT, March 2011, pp. 183-192. J. E. Fowler, S. Mun, and E. W. Tramel, “Multiscale Block Compressed Sensing with Smoothed Projected Landweber Reconstruction,” in Proceedings of the European Signal Processing Conference, Barcelona, Spain, August 2011, pp. 564-568. C. Chen, E. W. Tramel, and J. E. Fowler, “Compressed-Sensing Recovery of Images and Video Using Multihypothesis Predictions,” in Proceedings of the 45th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2011, pp. 1193-1198. S. Mun and J. E. Fowler “DPCM for Quantized Block-Based Compressed Sensing of Images,” in Proceedings of the European Signal Processing Conference, Bucharest, Romania, August 2012, pp. 1424-1428. S. Mun and J. E. Fowler, “Motion-Compensated Compressed-Sensing Reconstruction for Dynamic MRI,” in Proceedings of the International Conference on Image Processing, Melbourne, Australia, September 2013, pp. 1006-1010. Software bcs-spl-1.5-1.tar.gz – Matlab source code for BCS-SPL mc-bcs-spl-1.1-2.tar.gz – Matlab source code for MC-BCS-SPL ms-bcs-spl-1.2-2.tar.gz – Matlab source code for MS-BCS-SPL mh-bcs-spl-1.0-1.tar.gz – Matlab source code for MH-BCS-SPL bcs-spl-dpcm-1.0-2.tar.gz – Matlab source code for BCS-SPL-DPCM mc-cs-pl-1.0-1.tar.gz – Matlab source code for MC-CS-PL Support This material is based upon work supported by the National Science Foundation under Grant No. 0915307. Any opinions, findings and conclusions, or recomendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation (NSF). Last update: 7-sep-2024