The Research Of Compressive Sensing In The Image Compress-Reconsrtuction Field

Image is vision basic that people can apperceive the world. It is a important method that human can achieve,express and communicate information. When information science,computer science,engineering science and global science researcher study images, digital image processing technique is a valid tool. Digital image processing technique can also help people realize the world more external and more precise, images and graphs is the carrier of vision information. It could enlarge visual fields that people get information. There are many reasons that image data can be compressed. Original image data is highly related, so it has prodigious redundancies. Data redundancies lead to waste of bits, eliminating redundancies can retrench codes, and this is the aim of data compress. Biggish relativity about neighborhood pixels in the image is called space redundancy. Biggish relativity about fore-and-aft frames in the sequence image is called time redundancy.On the basis of Shannon theorem, this article study and demonstrate restricted isometry about compressive sensing matrix, then we give certain matrix structures that satisfy RIP, and prove the theorems, next study signal reconstruction theory and some related theorems, then prove some theorems,norm reliability and eigenvalues of random matrix, describe exact reconstruction principle, Fourier ensemble uniform uncertainty principle and uniform encode technology, prove restricted isometry property for random matrices about compressive sensing. At last based on foregoing study, we compressive sample and reconstruct one dimension signals,two dimension images,letters and combination of letters and images, and then we point out that compressive sensing can largely reduce data storage space and improve data transmission rate.