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

## 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.

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.

# 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

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