Research On Super-resolution Reconstruction Algorithm Of Optical Remote Sensing Image Based On Sparse Representation

Image super-resolution reconstruction is an enduring research topic in the fields of image processing, photogrammetry and remote sensing,and computer vision. Sparse representation theory, originated in the study of the visual nervous system, provides a new perspective for remote sensing image processing and causes widespread concern of scholars. It has become an international frontier and one of the focus in the fields of signal and image processing in recent years.This paper is supported by the national science and technology infrastructure "Data Sharing Infrastructure of Earth System Science——Data Sharing Infrastructure of Yangtze River Delta". Based on the sparse representation theory, this paper deeply studies on the super-resolution reconstruction methods of optical remote sensing image, and mainly revolves around two aspects of sparse representation theory, which includes dictionary learning model and algorithm, super-resolution reconstruction model and algorithm. The main contents and conclusions of the paper include:(1) The theoretical basis of remote sensing image sparse representation is studied. The neurophysiological background of sparse representation theory is expounded in this paper. The visual characteristics of human visual system provide a direct physiological basis for sparse representation of image, which promotes the development of image sparse representation theory. The relationship between the sparse characteristics of remote sensing image and its super-resolution reconstruction is analysised. The super-resolution reconstruction of single remote sensing image is a typical ill-posed problem. With sparsity as a prior for regularizing the ill-posed super-resolution problem, the problem can have a stable and unique solution. The expression methods of prior constraint of sparse are discussed and the sparse representation model of remote sensing image is studied. The types of dictionary in sparse representation theory is analyzed, which include analytical-based dictionary and learning-based dictionary. The analytical-based dictionary doesn’t need matrix multiplication, which has a fast computing speed but poor adaptability for the atoms are simple. Learning-based dictionary is trained from training samples by the machine learning methods, which has good adaptability and can represent the images with various features. But learning-based dictionary has weak structure and higher computational complexity.(2) The high-and low-resolution dictionaries learning methods are deeply studied. A joint dictionary learning model is given. By jointly learning the high-and low-resolution dictionaries, the two dictionaries are learned synchronously. A learning algorithm based on majorization minimization method for the joint learning model is presented. In which the original objective function is replaced by a surrogate objective function which is updated in each optimization step and can be easily minimized. The parameters in the surrogate functions are decoupled, so that the surrogate function can be minimized element-wise. And the method can guarantee to find local minima in each optimization step. A coupled dictionary learning method is studied. The high-and low-resolution dictionaries are studied in the high-and low-resolution feature space, respectively. And the two dictionaries have the same sparse representation. Based on this, a coupled sparse dictionary learning method is proposed, which spread the coupled thinking into sparse dictionary learning. As a result, on one hand it can ensure high-and low-resolution dictionaries have the same sparse representation, on the other hand, the dictionaries have good adaptability and compact structure. A loosely-coupled sparse dictionary learning model is proposed, in which the high-and low-resolution sparse dictionaries are learned, and the linear relationship between them is learned synchronously. For the strict requirement of the same sparse representation is further relaxed, the relationship between high-and low-resolution sparse dictionaries is more flexible.(3) The super-resolution reconstruction algorithms of single optical remote sensing image are deeply studied. A super-resolution reconstruction algorithm based on joint dictionary is given. In which the high-and low-resolution dictionary are used as prior knowledge to tutor super-resolution reconstruction for other images. Experimental results demonstrate the effectiveness of the sparsity as a prior for patch-based super-resolution. The algorithm can supply useful high-frequency information for super-resolution reconstruction of low-resolution remote sensing images under same area with same kind. A super-resolution reconstruction algorithm of remote sensing image based on pre-classified joint dictionary is studied. According to structural feature among atoms, the original dictionary is divided into several sub-dictionaries using improved K-means clustering algorithm, therefore, the sparse decomposition process of image patches is limited in a subset of learned dictionary with fewer number atoms. Experimental results show that the computation performance can be improved by50%, and the reconstruction quality can be assured, too.A super-resolution reconstruction algorithm of remote sensing image based on two coupled dictionaries is given, in which dictionary learning process and super-resolution reconstruction process are under the same feature space. Experimental results demonstrate the effectiveness of the algorithm. Compared with the super-resolution reconstruction algorithm based on joint dictionary, the reconstruction results of the two algorithms are close to each other, the difference between them is small. Which demonstrates the spatial difference has little influence on reconstruction results of the algorithm based on joint dictionary in another way. Based on this, a super-resolution reconstruction algorithm based on two coupled sparse dictionaries is proposed. On one hand, learning process of sparse dictionary and super-resolution reconstruction are ensured under the same feature space. On the other hand, the learned dictionaries not only has good adaptability but also has compact structure. Experimental results show the effectiveness of the algorithm. But the reconstruction effect is not ideal compared to the previous algorithm. After some analysis, the reason may be in association with construction of basic dictionary.A super-resolution reconstruction algorithm of remote sensing image based on two loosely-coupled sparse dictionaries is proposed. High-and low-resolution sparse dictionaries and the linear mapping relationship between them are conducted as prior knowledge for regularizing image super-resolution. Experimental results demonstrate that the loosely-coupled sparse dictionary learning method can outperform the joint dictionary learning method and the coupled dictionary learning method both quantitatively and qualitatively.