Journal Article

J. E. Fowler, S. Mun, and E. W. Tramel, “Block-Based Compressed Sensing of Images and Video,” Foundations and Trends in Signal Processing, vol. 4, no. 4, pp. 297-416, March 2012.

• Abstract:
  A number of techniques for the compressed sensing of imagery are surveyed. Various imaging media are considered, including still images, motion video, as well as multiview image sets and multiview video. A particular emphasis is placed on block-based compressed sensing due to its advantages in terms of both lightweight reconstruction complexity as well as a reduced memory burden for the random-projection measurement operator. For multiple-image scenarios, including video and multiview imagery, motion and disparity compensation is employed to exploit frame-to-frame redundancies due to object motion and parallax, resulting in residual frames which are more compressible and thus more easily reconstructed from compressed-sensing measurements. Extensive experimental comparisons evaluate various prominent reconstruction algorithms for still-image, motion-video, and multiview scenarios in terms of both reconstruction quality as well as computational complexity.

• Text:
• Source Code: See the BCS-SPL website.