圖像模糊外文翻譯---模糊圖像的盲反卷積復(fù)原算法-全文預(yù)覽
【正文】 n be degraded using lowpass filters and its noise. This 2 lowpass filter is used to blur/smooth the image using certain functions. Image restoration is to improve the quality of the degraded image. It is used to recover an image from distortions to its original image. It is an objective process which removes the effects of sensing environment. It is the process of recovering the original scene image from a degraded or observed image using knowledge about its nature. There are two broad categories of image restoration concept such as Image Deconvolution and Blind Image Deconvolution . Image Deconvolution is a linear image restoration problem where the parameters of the true image are estimated using the observed or degraded image and a known PSF (Point Spread Function). Blind Image Deconvolution is a more difficult image restoration where image recovery is performed with little or no prior knowledge of the degrading PSF. The advantages of Deconvolution are higher resolution and better quality. This paper is structured as follows: Section 2 describes the degradation model for blurring an image. Section 3 represents Canny Edge Detection. Section 4 describes the deblurring algorithm and overall architecture of this paper. Section 5 describes the sample results for deblurred images using our proposed algorithm. Section 6 describes the conclusion, parison and future work. 2 Degradation Model In degradation model, the image is blurred using filters and additive noise. Image can be degraded using Gaussian Filter and Gaussian Noise. Gaussian Filter represents the PSF which is a blurring function. The degraded image can be described by the following equation (1) *g H f n?? （ equation 1） In equation (1), g is degraded/blurred image, H is space invariant function (.) blurring function[3], f is an original image, and n is additive noise. The following represents the structure of degradation model. 3 Degradation Model Image deblurring can be done by the technique, Gaussian Blur. It is the convolution of the image with 2D Gaussian function. A) Gaussian Filter: Gaussian filter is used to blur an image using Gaussian function. It requires two parameters such as mean and variance. It is weighted blurring. Gaussian function is of the following form 2 2 22 / 2( , ) 1 / 2 * xyG x y e ??? ??? (equation 2) where ? is variance and x and y are the distance from the origin in the horizontal axis and vertical axis Gaussian Filter has an efficient implementation of that allows it to create a very blurry blur image in a relatively short time. B) Gaussian Noise: The ability to simulate the behavior and effects of noise is central to image restoration. Gaussian noise is a white noise with constant mean and variance. The default values of mean and variance are 0 and respectively. C) Blurring Parameter: The parameters needed for blurring an image are PSF, Blur length, Blur angle and type of noise. Point Spread Function is a blurring function. When the intensity of the observed point image is spread over several pixels, this is known as PSF. Blur length is the number of pixels by which the image is degraded. It is number of pixel position is shifted from original position. 4 Blur angle is an angle at which the image is degraded. Available types of noise are Gaussian noise, Salt and pepper no
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