Abstract
A perceptual-based compressed sensing (CS), which focuses the measurements and the recovery on the visually important low-frequency coefficients, is applied for multi-view image signals. High correlation among different views is exploited to generate signal prediction using disparity estimation and compensation techniques. A residual-based recovery is utilised as a joint recovery for the non-reference images to enhance the reconstruction performance. The proposed framework shows remarkable performance improvement over the conventional CS with joint recovery as well as the perceptual-based CS with independent recovery.
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