Compressed sensing is applied to multiview image sets and inter- image disparity compensation is incorporated into image recon- struction in order to take advantage of the high degree of inter- image correlation common to multiview scenarios. Instead of re- covering images in the set independently from one another, two neighboring images are used to calculate a prediction of a tar- get image, and the difference between the original measurements and the compressed-sensing projection of the prediction is then re- constructed as a residual and added back to the prediction in an iterated fashion. The proposed method shows large gains in per- formance over straightforward, independent compressed-sensing recovery. Additionally, projection and recovery are block-based to signicantly reduce computation time.
Mohamed Farhat, Philippe Reymond
Lyesse Laloui, Alessio Ferrari, Jose Antonio Bosch Llufriu
Yves Perriard, Yoan René Cyrille Civet, Thomas Guillaume Martinez, Florian Fernand Hartmann, Simon Holzer, Fatma Öz