Adaptive compressed sensing of color images based on salient region detection

We propose a novel algorithm for color image compressed sensing (CS). Our method involves the adaptive measurement and reconstruction of color images based on visual saliency detection. First, we divide the image into blocks and transform the RGB channel into the YUV channel. Secondly, we use statistical texture distinctiveness to calculate the saliency of each block and normalize energy, thereby establishing an adaptive measurement rate and measurement matrix. Thirdly, we adaptively measure the Y channel according to block prominence and preserve the information of the UV channel. During reconstruction, we utilize adaptive block measurement rate to re-estimate block saliency and then reconstruct the objective function of the weighted reconstruction model according to the re-estimated block saliency. Finally, we combine the reconstructed Y channel with the reserved UV channel to obtain the final image. Experimental results show that compared with other state-of-the-art approaches, the proposed algorithm can not only provide good subjective visual quality but can also present higher peak signal to noise ratio (PSNR) under the same sampling rate.