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CS代写|图像处理作业代写Image Processing代考|Blur Kernel Estimation

According to the image degradation model, to restore the blurred image (de-blurring), it is necessary to determine the blur function, that is, estimate the blur kernel. In practice, the blur function is often difficult to be completely determined from the image, but it can be estimated with the help of some prior knowledge. In the case that the blur function cannot be obtained directly, performing image restoration to eliminate blur is also called blind de-convolution.
The estimation methods for blur functions can be divided into three categories:

  1. Estimation with the help of image observation.
  2. Estimation by using point source image experiment.
  3. Estimation with the help of modeling degradation.
    3.1.3.1 Using the Image Observation to Estimate the Blur Function
    Consider the case where the image is affected by linear space invariant degradation. If only a degraded image $g(x, y)$ is given without any knowledge about the image degradation function, only the information contained in this image can be used to estimate the degradation function.

When the degradation is caused by the blur process, a (sub) region with a typical structure in the image can be selected. To reduce the influence of noise, the region should preferably contain obvious edges or high-contrast borders between the object and the background. If the grayscale contrast between the object and the background is $C_{o b}$, and the mean square error of the noise is $\sigma$, then the signal-to-noise ratio can be defined as (Kitchen and Rosenfeld 1981).
$$
\mathrm{SNR}=\left(\frac{C_{o b}}{\sigma}\right)^2
$$
Here, it is required to choose a region with a large signal-to-noise ratio.
Suppose the region in the selected blurred image is $g_s(x, y), g_s(x, y)$ needs to be processed to obtain $f_s(x, y)$, where $f_s(x, y)$ is the estimation of the original image $f(x, y)$ at the position corresponding to $g_s(x, y)$.

CS代写|图像处理作业代写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 be established, or the process of image degradation affected by 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.

CS代写|图像处理作业代写Image Processing代考|EEE6512

图像处理代考

CS代写|图像处理作业代写Image Processing代考|Blur Kernel Estimation

根据图像退化模型,要恢复模糊图像(去模糊),需要确定模糊函数,即估计模糊核。在实践中,模糊函数通常很难从图像中完全确定,但可以借助一些先验知识进行估计。在不能直接得到模糊函数的情况下,进行图像复原以消除模糊也称为盲反卷积。
模糊函数的估计方法可以分为三类:

  1. 借助图像观察进行估计。
  2. 利用点源图像实验进行估计。
  3. 在建模退化的帮助下进行估计。
    3.1.3.1 使用图像观察估计模糊函数
    考虑图像受到线性空间不变退化影响的情况。如果只有退化的图像G(X,是)在没有任何关于图像退化函数的知识的情况下给出,只有该图像中包含的信息可以用来估计退化函数。

当退化是由模糊过程引起的时,可以选择图像中具有典型结构的(子)区域。为了减少噪声的影响,该区域最好在物体和背景之间包含明显的边缘或高对比度的边界。如果物体与背景的灰度对比为C○b,噪声的均方误差为p, 那么信噪比可以定义为 (Kitchen and Rosenfeld 1981)。

小号ñR=(C○bp)2
这里需要选择信噪比大的区域。
假设所选模糊图像中的区域为Gs(X,是),Gs(X,是)需要处理才能获得Fs(X,是), 在哪里Fs(X,是)是对原始图像的估计F(X,是)在对应的位置Gs(X,是).

CS代写|图像处理作业代写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恢复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。图像恢复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。它们之间的区别在于,图像增强技术一般只利用人类视觉系统的特性来获得好看的视觉效果,而图像修复则认为图像(质量)在一定的环境/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来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。无约束复原的方法仅将图像视为一个数字矩阵,没有考虑图像复原后应受到的物理约束,主要从数学的角度来处理。约束复原法还考虑到复原后的图像应该受到一定的物理约束,例如空间上比较平滑,图像灰度值始终为正。

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。如果可以建立一定的噪声模型,或者可以对噪声影响的图像退化过程进行建模,那么也可以基于噪声退化模型,利用图像复原技术对图像进行去噪处理。

在上一节建立的基本图像退化模型的基础上,下面总结了三种典型的方法,即逆滤波恢复、维纳滤波恢复和约束最小二乘恢复(Zhang 2017)。此外,还通过实例介绍了如何利用人机交互方式提高图像复原的灵活性和效率。

CS代写|图像处理作业代写Image Processing代考

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