Research on the natural image super-resolution reconstruction algorithm based on compressive perception theory and deep learning model

With the bursting development of machine learning and artificial intelligence, the pattern recognition based image processing techniques are growing faster than ever before. In this paper, we conduct theoretical analysis on the natural image super-resolution reconstruction algorithm based on compressive perception theory and deep learning model. The image restoration is the purpose of the degraded image processing which make its recovery as it had been before the degradation of ideal image. According to the views of Fourier optics, optical imaging system is a low pass filter, due to the general influence of optical diffraction. The deep neural network with hierarchical unsupervised training method stratified greed training beforehand matter will be the result of the training as the novel learning supervision probability model of the initial value to make good use of the optical imaging system. The adopted compressed sensing theory points out that as long as signal is compressible or sparse, so, if there is a transformation matrix is not related observation matrix on signal can directly obtain compressed form of the original signal. Our research adopts the advances of the mentioned technique, in the training step, we use deep neural network to automatically capture the features and in the reconstruction procedure we use the compressive sensing and dictionary learning theory to reconstruct the high resolution image. By enhancing both of the steps, our experimental result indicates the feasibility of the novel algorithm. The prospect is also discussed in the final part.