WITHDRAWN: Image Art Design Based on Image Compression Sensing System and Embedded Device

Abstract

ABSTRACT The image Compressed Sensing (CS), two main challenges is to develop the design and reconstruction of the sample matrix. In one aspect, a random sample matrix is generally used and signal characteristics of the signal independent negligible. On the other hand, the most advanced art design image CS method made a very good performance. High computational complexity to meet two challenges recommend using convolution neuroimaging CS architecture. Network includes sampling network (called Convolutional Neural Network (CNN)) Joint Optimization of network reconstruction. Adaptation sampling learning image samples from a training matrix, which can accommodate a plurality of images of the configuration information, better reconstruction CS measurements. Reconstruction network, the network including linear and nonlinear initial reconstructed depth reconstruction networks, learning measuring terminal between CS and the reconstructed image to the end of the map. While achieving high speed of operation, the experimental results show that the most advanced reconstruction quality provided by the state. In addition, the results also show that the trained sample matrix can significantly improve the traditional CS image reconstruction method.