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计算机代写|图像处理代写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.

计算机代写|图像处理代写Image Processing代考|Blurring Degradation

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.

Figure 3.3a shows an original clear image and its two corresponding images with motion blur (Figure 3.3b) and out-of-focus blur (Figure 3.3c). The upper left corner of Figure 3.3b and c shows the blur kernel (image) that produces the corresponding blur. It can be seen that the blur kernel of motion blur is a straight line reflecting the motion trajectory (it will be a curve for complex motion), and the blur kernel of out-of-focus blur is a disc. Motion blur is directional, while out-of-focus blur is more consistent in all directions. The difference in the blur kernel here corresponds to the blurring of the scene in Figure 3.3b and $c$.
In addition, atmospheric turbulence blur contains some of the characteristics of the above two kinds of blur in a certain sense. Atmospheric turbulence is an important form of motion in the atmosphere.

计算机代写|图像处理代写Image Processing代考|EEE6512

图像处理代考

计算机代写|图像处理代写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。原始地震数据被噪声和不需要的反射严重污染,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,而且通常是在采集数据时完成的。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)。

为降噪过程、质量控制提供信心(问C)是必须的。它可以确保不需要代价高昂的数据重新获取。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,经过训练可以对三种类型的过滤进行分类(最佳 = 过滤良好;苛刻 = 过度过滤,信号失真;温和 = 过滤不足,噪声保留在数据中)。这些属性是从数据中计算出来的,并在地理物理上表示为过滤质量的统计度量。

实验结果表明,某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集群很接近,因为它们反映了之前对两种类型过滤之间的细微差别所做的观察。

在该系统中,多层感知器(MLP)用于分类。基于在没有正则化的情况下,最佳 MLP 结构随着深度的增加而趋于过度拟合的基本假设,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描述了一对一二元分类过程的流程图。训练集和验证集被分成钾使用 bootstrap 聚合策略创建相同大小的子集。训练和验证子集被转换为否=3二进制集。然后,二元 MLP 生成器用于预测二元决策子空间。然后将预测的班级成员反馈给投票系统,该系统根据多数票决定班级,从而产生更准确的分类结果。

计算机代写|图像处理代写Image Processing代考|Blurring Degradation

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。此运动可能源自相机运动(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,导致成像过程中运动轨迹的长度达到像素级,就会在拍摄图像上形成可见的运动模糊。运动模糊体现在图像中景物沿运动方向拉伸,产生重影,故又称运动拖影。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能清晰成像的最近物体和最远物体之间的距离。当镜头对焦到一定距离时,该距离的景物最为清晰,偏离该距离的景物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,模糊不会达到像素级别,产生的模糊是察觉不到的;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),

图 3.3a 显示了原始清晰图像及其两个对应的具有运动模糊(图 3.3b)和离焦模糊(图 3.3c)的图像。图 3.3b 和 c 的左上角显示了产生相应模糊的模糊内核(图像)。可以看出,运动模糊的模糊核是反映运动轨迹的直线(复杂的运动会是曲线),离焦模糊的模糊核是圆盘。运动模糊是有方向性的,而离焦模糊在所有方向上都更加一致。这里模糊核的不同对应于图3.3b中场景的模糊和C.
另外,大气湍流模糊在一定意义上包含了上述两种模糊的一些特征。大气湍流是大气运动的一种重要形式。

计算机代写|图像处理代写Image Processing代考

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