In this paper, a sampling adaptive for block compressed sensing with smooth projected Landweber based on edge detection （SA-BCS-SPL-ED） image reconstruction algorithm is presented. This algorithm takes full advantag...In this paper, a sampling adaptive for block compressed sensing with smooth projected Landweber based on edge detection （SA-BCS-SPL-ED） image reconstruction algorithm is presented. This algorithm takes full advantage of the characteristics of the block compressed sensing, which assigns a sampling rate depending on its texture complexity of each block. The block complexity is measured by the variance of its texture gradient, big variance with high sampling rates and small variance with low sampling rates. Meanwhile, in order to avoid over-sampling and sub-sampling, we set up the maximum sampling rate and the minimum sampling rate for each block. Through iterative algorithm, the actual sampling rate of the whole image approximately equals to the set up value. In aspects of the directional transforms, discrete cosine transform （DCT）, dual-tree discrete wavelet transform （DDWT）, discrete wavelet transform （DWT） and Contourlet （CT） are used in experiments. Experimental results show that compared to block compressed sensing with smooth projected Landweber （BCS-SPL）, the proposed algorithm is much better with simple texture images and even complicated texture images at the same sampling rate. Besides, SA-BCS-SPL-ED-DDWT is quite good for the most of images while the SA-BCS-SPL-ED-CT is likely better only for more-complicated texture images.展开更多
基金supported by the National Natural Science Foundation of China (61071091, 61071166)the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institution-Information and Communication Engineering
文摘In this paper, a sampling adaptive for block compressed sensing with smooth projected Landweber based on edge detection （SA-BCS-SPL-ED） image reconstruction algorithm is presented. This algorithm takes full advantage of the characteristics of the block compressed sensing, which assigns a sampling rate depending on its texture complexity of each block. The block complexity is measured by the variance of its texture gradient, big variance with high sampling rates and small variance with low sampling rates. Meanwhile, in order to avoid over-sampling and sub-sampling, we set up the maximum sampling rate and the minimum sampling rate for each block. Through iterative algorithm, the actual sampling rate of the whole image approximately equals to the set up value. In aspects of the directional transforms, discrete cosine transform （DCT）, dual-tree discrete wavelet transform （DDWT）, discrete wavelet transform （DWT） and Contourlet （CT） are used in experiments. Experimental results show that compared to block compressed sensing with smooth projected Landweber （BCS-SPL）, the proposed algorithm is much better with simple texture images and even complicated texture images at the same sampling rate. Besides, SA-BCS-SPL-ED-DDWT is quite good for the most of images while the SA-BCS-SPL-ED-CT is likely better only for more-complicated texture images.
