计算机代写|图像处理代写Image Processing代考|ECE867

计算机代写|图像处理代写Image Processing代考|Using the Point Source Image Experiment to Estimate the Blur Function

If the type of equipment that collects the blurred image $g(x, y)$ is known and one has similar equipment on hand, it is possible to make a more accurate estimation of the blur. First, use the equipment on hand to make different system settings or parameter selections and try to obtain an image close to the given blurred image. Next, according to the same system settings or parameter selection, a small spot (approximately one pulse) is imaged to obtain the impulse response of the blur process (the characteristics of the linear space invariant system are completely determined hy its impulse response).

An image can be regarded as a collection of multiple point source images. For example, if the point source image is regarded as the approximation of the unit impulse function $(F[\delta(x, y)]=1)$, then there is $G(u, v)=H(u, v) F(u, v) \approx H(u, v)$. In other words, the transfer function $H(u, v)$ of the blur system at this time can be approximated by the Fourier transform of the blurred image.

In practical applications, it is hoped that the small light spot should be as bright as possible, and the contrast with the background should be as large as possible so that the influence of noise can be reduced to a minimum or even negligible. Because the Fourier transform of a pulse is a constant (here set to $C$ ), according to Equation (3.8), one can write
$$H(u, v)=\frac{G(u, v)}{C}$$
In the equation, $G(u, v)$ is the Fourier transform of the blurred image $g(x, y)$, and $H(u, v)$ is the blur function.

计算机代写|图像处理代写Image Processing代考|IMAGE RESTORATION AND DE-BLURRING

Research on de-blurring has a history of many years, and people have proposed a variety of classic methods, which have been widely used in practice. The following paragraph briefly introduces the methods based on image restoration technology to eliminate blur.

Image restoration is a large category of technology in image processing. Image restoration is closely related to image enhancement. The similarity between image restoration and image enhancement is that they can both improve the visual quality of the input image. The difference between them is that image enhancement technology generally only uses the characteristics of the human visual system to obtain good-looking visual results, while image restoration considers that the image (quality) is degraded or deteriorated under certain circumstances/conditions (i.e., the image quality has been reduced and distorted), and now it is necessary to reconstruct or restore the original image based on the corresponding degradation model and knowledge. In other words, the image restoration technology is to model the image degradation process and restore it according to the determined image degradation model to obtain the original desired effect.

Under the conditions of a given model, image restoration techniques can be divided into two categories: unconstrained and constrained. The method of unconstrained restoration only regards the image as a digital matrix, without considering the physical constraints that the image should be subjected to after restoration, and mainly deals with it from a mathematical point of view. The constrained restoration method also considers that the restored image should be subject to certain physical constraints, such as being relatively smooth in space and the image gray value is always positive.

By the way, although noise is random, it often has certain statistical laws. If a certain model of noise can he established, or the process of image degradation affected hy noise can be modeled, then image restoration technology can also be used to denoise the image based on the noise degradation model.

Based on the basic image degradation model established in the previous section, the following summarizes three typical methods, namely, inverse filter restoration, Wiener filter restoration, and constrained least squares restoration (Zhang 2017). In addition, it also introduces, with examples, how to use human-computer interaction methods to improve the flexibility and efficiency of image restoration.

图像处理代考

计算机代写|图像处理代写Image Processing代考|Using the Point Source Image Experiment to Estimate the Blur Function

$$H(u, v)=\frac{G(u, v)}{C}$$

计算机代写|图像处理代写Image Processing代考|IMAGE RESTORATION AND DE-BLURRING

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