The Study And Realization About The Improved Restoration Algorithm Of Degraded Image

Basing on the difference of vision influence brought, degraded image can be divided into two species: blur image and distortion image, so the restoration algorithm aiming at these images is a hotpot of the digital signal process and the paper also does many researches about this field.The key of the image restoration problem is to obtain the prior knowledge of point spread function (PSF) or its Fourier Transform. If the estimate of PSF is inaccurate, the restoration image will be much worse. The original restoration methods are all based on the knowledge of PSF, they have the advantages of high restoration quality and fast computation. But the actual PSF of the system cannot be obtained usually which restricts the application of these methods. In the situation of unknown the knowledge of PSF, blind image restoration methods can restoration the blur image by estimating the PSF. But they have some false, such as large calculation,the large estimating error of PSF which influence the restoration quantity and speed.In order to overcome these problems, an adaptive blind image restoration method is proposed. According to the feature that the certain blur may lead to the specific frequency component distortion of the image Fourier spectrum, we can automaticly classify the types of motion blur,defocus blur,gaussian blur and others by spectrum conversion and feature extraction. And then, we estimate the usual types of PSF with corresponding method and restore it with the typical method, while for others, we use an improved NAS-RIF blind restoration algorithm. The new method combines the typical methods and blind methods of image restoration, not only reduces the calculation quantity, but also has strong effectivity and good adaptivity.Based on the research of the methods of identification of motion blur direction from motion blurred image, an improved method is proposed. First, convoluting with 3x3 direction derivation matrix, the motion blur image is derivatived, and then gray level transformation is applied to the values of the pixels of directional derivative of the image. Finally, motion direction is identified automatically by measuring the direction where the summation of the absolute transformed values of the pixels of the image derivative. The experimental results show that the improved method can not only improve the calculate precision, but also expand the application of the method.The distorted images especially non-line distorted images have not been done well for the complexity of non-line. The normal distortion correction method for distorted images which obtains distortion coefficients by setting up a distortion model, but as the calculation is complicated and numerical error becomes a big problem.The image of nonlinear distortion was researched through the research of non-line distorted. The relationship of the distortion image and the normal image had been built via the input-output data gained by the trained neural networks. And the nonlinear distorted image correction based on neural was reached.