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CS代写|图像处理作业代写Image Processing代考|Classify Noise Filtering Results in Seismic Images

Seismic imaging is the main technology used for subsurface hydrocarbon prospection. The raw seismic data is heavily contaminated with noise and unwanted reflections that need to be removed before further processing. Therefore, the noise attenuation is done at an early stage and often while acquiring the data. Remote sensing images are often affected by noise in the process of digitization and transmission processes. De-noising is an indispensable way of improving image quality (Wu et al. 2020).

To give confidence in the de-noising process, quality control $(\mathrm{QC})$ is required. It can ensure that a costly data re-acquisition is not needed. A supervised learning approach to build an automatic QC system is proposed (Mejri and Bekara 2020). The QC system is an attribute-based classifier that is trained to classify three types of filtering (optimal = good filtering; harsh = over filtering, the signal is distorted; mild = under filtering, noise remains in the data). The attributes are computed from the data and represented geo-physically for the statistical measures of the quality of the filtering.

Experimental results show that some attributes show a good level of visual separation between the different types of filtering, particularly the harsh one. The clusters of attributes for the mild and the optimal filtering are close as they reflect the observation made earlier about the subtle differences between the two types of filtering.

In this system, multi-layer perceptron (MLP) is used for classification. Based on the fundamental assumption that without regularization, an optimal MLP structure tends to over fit as its depth is increased, a secure MLP-building strategy is adopted to build the multilayer perceptron model with the least validation cross-entropy error.

Figure $2.27$ depicts the flowchart of the one-vs-all binary classification process. The training and the validation sets were split into $K$ subsets of equal size using the bootstrap aggregation strategy. The training and validation subsets were converted to $N=3$ binary sets. Then, the binary MLP generator is used to predict binary decision subspaces. The predicted class members are then fed forward to a voting system, which decides on the class based on the majority vote, resulting in more accurate classification results.

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

Image blur is a common image degradation situation or process, which can also be represented by the model shown in Figure 3.1, where the blur is generated by the system $H$, which is also called the blur kernel at this time. In Figure 3.1, the input image is affected by both blur and noise. As a result, the degraded image $g(x, y)$ becomes a noisy blurred image.
If the blur kernel is known, only the clear image needs to be solved. This problem can be called non-blind de-blurring. If both the blur kernel and the clear image need to be solved, this problem is called blind de-blurring. For the blind de-blurring problem, if the blur kernel can be obtained, the problem can be transformed into a non-blind de-blurring problem.
Solving the blind de-blurring problem directly is an ill-conditioned (underdetermined) problem because the solution is not unique. In the actual solving process, it is necessary to introduce prior knowledge about the blur kernel or clear image (including the heuristic knowledge of enhancing the edge and the knowledge of constructing the prior probability distribution model). According to the mathematical method to solve the underdetermined problem, one can consider constructing a regularized cost function based on the prior information of the image to transform the problem into a variational problem, in which the variational integral depends on the data and smoothing constraints at the same time. For example, for the problem of estimating function $f$ from a set of values $y_1, y_2, \ldots, y_n$ at points $\boldsymbol{x}1, \boldsymbol{x}_2, \ldots, \boldsymbol{x}_n$, the regularization method is to minimize the functional $$ H(f)=\sum{i=1}^N\left[f\left(x_i\right)-y_i\right]^2+k \Phi(f)
$$
In the equation, $\Phi(f)$ is a smoothing functional, and $k$ is a positive parameter, which is called a regularization number.

The blur caused by different reasons will have different effects on the image quality (different changes in the image), and the blur kernels corresponding to different types of blur can also be very different.

The necessary condition for motion blur is that there is relative motion between the camera and the object during the imaging process. This motion can originate from camera motion (global motion), object motion (local motion), or both. In practice, if the imaging time is long and/or the motion is relatively violent, resulting in the length of the trajectory of the motion reaching the pixel level during the imaging process, visible motion blur will be formed on the captured image. Motion blur is embodied in the image that the scenery stretches along the direction of motion and produces double shadows, so it is also called motion smear. The image acquisition system (with a narrow field of view) that uses a telescope lens is very sensitive to this type of image degradation.

The out-of-focus blur is related to the depth of field of the camera lens. The depth of field of the lens corresponds to the distance between the closest object and the farthest object that can be clearly imaged in the scene. When the camera lens is focused at a certain distance, the scene at that distance is the clearest, and the scene deviating from this distance will gradually blur with the degree of deviation. In general, within a certain range (depth of field) before and after this distance, the blur does not reach the pixel level, and the resulting blur cannot be noticed; the scene beyond this range will show the blur effect on the collected image. This blur is generally isotropic, which limits the resolution sharpness of image. Therefore, if it does not focus on the object one wants to observe (missing focus), the object in the image may not be clear enough.

CS代写|图像处理作业代写Image Processing代考|GPY470

图像处理代考

CS代写|图像处理作业代写Image Processing代考|Classify Noise Filtering Results in Seismic Images

地震成像是用于地下油气勘探的主要技术。原始地震数据受到噪声和不需要的反射的严重污染,需要在进一步处理之前将其去除。因此,噪声衰减是在早期阶段完成的,通常是在获取数据时完成的。遥感影像在数字化和传输过程中经常受到噪声的影响。去噪是提高图像质量不可或缺的方法(Wu et al. 2020)。

让人们对降噪过程充满信心,质量控制(问C)是必须的。它可以确保不需要昂贵的数据重新采集。提出了一种构建自动 QC 系统的监督学习方法(Mejri 和 Bekara 2020)。QC 系统是一个基于属性的分类器,经过训练可对三种类型的过滤进行分类(最佳 = 良好过滤;苛刻 = 过度过滤,信号失真;轻度 = 过滤不足,数据中仍然存在噪声)。这些属性是从数据中计算出来的,并在地球物理上表示,用于过滤质量的统计测量。

实验结果表明,一些属性在不同类型的过滤之间表现出良好的视觉分离水平,尤其是苛刻的过滤。温和过滤和最佳过滤的属性簇很接近,因为它们反映了之前对两种过滤类型之间细微差异的观察。

在这个系统中,多层感知器(MLP)用于分类。基于没有正则化的基本假设,最优的 MLP 结构会随着深度的增加而过度拟合,采用安全的 MLP 构建策略来构建具有最小验证交叉熵误差的多层感知器模型。

数字2.27描述了一对多二进制分类过程的流程图。训练集和验证集分为ķ使用引导聚合策略的大小相等的子集。训练和验证子集被转换为ñ=3二进制集。然后,二元 MLP 生成器用于预测二元决策子空间。然后将预测的类成员前馈到投票系统,该系统根据多数票决定类,从而产生更准确的分类结果。

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

图像模糊是一种常见的图像退化情况或过程,也可以用图 3.1 所示的模型来表示,其中模糊是由系统产生的H,此时也称为模糊核。在图 3.1 中,输入图像同时受到模糊和噪声的影响。结果,退化的图像G(X,是)变成嘈杂的模糊图像。
如果模糊核已知,则只需要求解清晰的图像。这个问题可以称为非盲去模糊。如果模糊核和清晰图像都需要解决,这个问题称为盲去模糊。对于盲去模糊问题,如果可以获得模糊核,则可以将问题转化为非盲去模糊问题。
直接解决盲目去模糊问题是一个病态(欠定)问题,因为解决方案不是唯一的。在实际求解过程中,需要引入关于模糊核或清晰图像的先验知识(包括增强边缘的启发式知识和构建先验概率分布模型的知识)。根据求解欠定问题的数学方法,可以考虑基于图像的先验信息构造正则化代价函数,将问题转化为变分问题,变分积分取决于数据和平滑约束同时。例如,对于估计函数的问题F从一组值是1,是2,…,是n在点X1,X2,…,Xn, 正则化方法是最小化泛函

H(F)=∑一世=1ñ[F(X一世)−是一世]2+ķ披(F)
在等式中,披(F)是一个平滑泛函,并且ķ是一个正参数,称为正则化数。

不同原因造成的模糊会对图像质量产生不同的影响(图像的不同变化),不同类型的模糊对应的模糊核也可以有很大的不同。

运动模糊的必要条件是在成像过程中相机与物体之间存在相对运动。该运动可以源自相机运动(全局运动)、对象运动(局部运动)或两者。在实际应用中,如果成像时间较长和/或运动比较剧烈,导致在成像过程中运动轨迹的长度达到像素级,就会在拍摄的图像上形成可见的运动模糊。运动模糊体现在景物沿运动方向伸展并产生双重阴影的图像中,因此也称为运动拖影。使用望远镜镜头的图像采集系统(具有窄视场)对这种类型的图像退化非常敏感。

离焦模糊与相机镜头的景深有关。镜头的景深对应于场景中可以清晰成像的最近物体和最远物体之间的距离。当相机镜头对焦在一定距离时,该距离的场景最清晰,偏离这个距离的场景会随着偏离的程度逐渐模糊。一般来说,在这个距离前后的一定范围(景深)内,模糊都达不到像素级,由此产生的模糊是无法察觉的;超出此范围的场景将在采集的图像上显示模糊效果。这种模糊通常是各向同性的,这限制了图像的分辨率清晰度。因此,如果它没有聚焦在想要观察的物体上(失焦),

CS代写|图像处理作业代写Image Processing代考

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