Oriented Iiccd Camera Is Not Completely Random Sampling Of Remote Sensing Image Reconstruction Algorithm

Because the Image Intensified CCD (IICCD) camera works in dim light conditions, with high gain, high SNR, a lower illumination level in the work, it is widely used in remote sensing and military. However, it is inevitable that IICCD camera arises the degradation of image quality because of optical blur, noise and other disturbances and high-resolution image data transmission pressure increases, so people come to pay close attention to the highly incomplete measurements technique in remote sensing image restoration and reconstruction. Currently, the sparse representation and regularization method is an international research focus for image restoration theory and algorithm.In this paper, the summary of the current compressed sensing and image restoration technique is given. Moreover variation regularization image restoration is the main core and the reconstruction model and algorithm of image restoration in complete sampling and incomplete sampling is discussed.The primary contributions of this dissertation contain the following points:Firstly, total variation (TV) regularization image restoration coupled model based on sparsity constraint is proposed. This model is composed of TV regularization restoration model, curvelet transform sparsity of the image and data fidelity model. It is good at restoring the edge and texture of the image. Combined with operator splitting method, a numerical algorithm of the optimization model is given, which is a multi-step iterative algorithm. Experiments show that the algorithm is superior to the fast TV (FTVdG) algorithm in visual quality of the restored image.Secondly, for remote sensing image restoration (deblurring) in incompletely random sampling, an image restoration and reconstruction algorithm based on Poisson singular integral and Curvelet threshold shrinkage (Curvelet-PSI) is designed and implemented. Anew image restoration and reconstruction algorithm based Fourier shrinkage and Curvelet Iteration threshold shrinkage (FoRD) is proposed. The restoration and reconstruction capacity of the new algorithm Curvelet-FoRD is equivalent in Curvelet-PSI. However, it requires less parameter, and the parameter adjustment is easy and simple. Moreover, it is fast implemented.Thirdly, for IICCD camera system, the comprehensive analysis of the IICCD camera imaging mechanism is done, and the IICCD image degradation model is established according to the optical transfer function and noise characteristics. The IICCD image restoration algorithm based on TV-l1is proposed in completely sampling.Moreover, the Curvelet-FoRD IICCD image restoration and reconstruction algorithm in incompletely random sampling is designed and given. Then experimental results show these algorithms are effective.