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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.

计算机代写|图像处理代写Image Processing代考|ECE867

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

如果收集模㗅图像的设备类型 $g(x, y)$ 是已知的并且手头有类似的设备,可以对模糊进行更准确的估计。 首先,利用手头的设备进行不同的系统设置或参数选择,尝试得到接近给定模胡图像的图像。接下来, 根据相同的系统设置或参数选择,对一个小光点(约一个脉冲)进行成像,得到模胡过程的脉冲响应 (线性空间不变系统的特性完全由其脉冲响应决定)。
一幅图像可以看作是多个点源图像的集合。例如,如果将点源图像看成是单位冲激函数的逼近 $(F[\delta(x, y)]=1)$ ,那么有 $G(u, v)=H(u, v) F(u, v) \approx H(u, v)$. 换句话说,传递函数 $H(u, v)$ 此时模 糊系统的 可以通过模脨图像的傅里叶变换来近似。
在实际应用中,希望小光斑尽可能亮,与背景的对比度尽可能大,使噪声的影响降到最低甚至可以忽略 不计。因为脉冲的傅里叶变换是一个常数 (这里设置为 $C$ ),根据式(3.8),可写
$$
H(u, v)=\frac{G(u, v)}{C}
$$
在等式中, $G(u, v)$ 是模楜图像的傅里叶变换 $g(x, y)$ ,和 $H(u, v)$ 是模糊函数。

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

去模糊的研究已有多年历史,人们提出了多种经典方法,并在实践中得到广泛应用。下面简要介绍基于图像复原技术消除模糊的方法。

图像复原是图像处理中的一大类技术。图像复原与图像增强密切相关。图像恢复和图像增强的相似之处在于它们都可以提高输入图像的视觉质量。它们的区别在于,图像增强技术一般只利用人类视觉系统的特征来获得好看的视觉效果,而图像复原则考虑图像(质量)在一定情况/条件下(即图像质量已经降低和失真),现在需要根据相应的退化模型和知识重建或恢复原始图像。换句话说,

在给定模型的条件下,图像恢复技术可以分为两类:无约束和有约束。无约束复原方法只是把图像看成一个数字矩阵,没有考虑图像复原后应该受到的物理约束,主要从数学的角度来处理。约束复原法还考虑到复原图像应受到一定的物理约束,如空间相对平滑、图像灰度值始终为正等。

顺便说一句,虽然噪声是随机的,但它往往具有一定的统计规律。如果能够建立某种噪声模型,或者可以对图像受噪声影响的退化过程进行建模,那么也可以利用图像复原技术,基于噪声退化模型对图像进行去噪。

基于上一节建立的基本图像退化模型,下面总结三种典型的方法,即逆滤波器复原、维纳滤波器复原和约束最小二乘法复原(Zhang 2017)。此外,还结合实例介绍了如何利用人机交互的方式来提高图像复原的灵活性和效率。

计算机代写|图像处理代写Image Processing代考

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