A Research Of Image Fusion And Image Compression Coding Based On Compressed Sensing

Image processing is an important research because people sense the outsideworld mainly from image. Image fusion, as a process of combining multiple imagesfrom different sensors can provide more clear and effective image for the furtherimage processing applications. Therefore, it is widely used in military, medical imageprocessing, remote sensing and so on. With the rapid development of informationtechnology, and the raising demand for high-definition, image, it will inevitably incurthe daunting cost of image aquisition, image storage and image transmission. Thus,image compression will become an important research direction.Considering the problems in the image fusion and image compression coding,the thesis proposes the new methods based on compressed sensing. The main work areas follows:Firstly, the thesis introduces the advance of the image fusion and imagecompression coding technology, and then reviews the theoretical framework and it’sapplication of compressed sensing theory in two-dimension image.Secondly, based on compressed sensing, a new image fusion method ispresented. The new method can take reduced samples, and has the advantages ofsimple structure and easy implementation. The method decomposes two or moreoriginal image using wavelet transform first, and gets the sparse matrix by the waveletcoefficient sparse representation, and fuses the sparse matrixs with the coefficientsabsolute value maximum scheme. With randomly observed, it can receive thecompressed sample. At the fusing end, the fusion image is recovered from the reducedsamples by solving the optimization. The proposed method can construct the fusionimage with less measurements because the wavelet coefficients are spare presentation.Simulation results show the proposed method exhibits its superiority over thetraditional method of the maximum absolute values fusion with the same samplingrates, and under the lower sampling, it can also achieve better fusion performance.Finally, for the problem in vector quantization that the compression ratio is slowand image restoration result is not satisfactory, and according to the sparsityproperties of the high frequency wavelet transform coefficients, the thesis proposed anew method of categories quantization coding for image based on compressed sensing.Compared with the LBG vector quantization coding algorithm, simulation results demonstrated that the proposed algorithm improved the quality of the recoveredimage significantly. For the similar compression ratio, the peak signal to noise ratio ofthe proposed algorithm was improved about2~4dB. For the similar PSNR, thealgorithm in image compression has also improved significantly.