The texture information of seismic images is directly related to stratigraphic information. Chaotic or thoughtless or superimposed patterns are simple patterns of texture. Some of the techniques proposed for seismic texture analysis are briefly presented. The Cosine Transform Discrete Image Compression (DCT) algorithm has been widely implemented in DSP chips, with many companies developing DSP chips based on DCT technology. DCTs are widely used for encoding, decoding, video encoding, audio encoding, multiplexing, control signals, signaling, analog-to-digital conversion, luminance and color difference formatting, and color formats such as YUV444 and YUV411. DCT are also used for encoding operations such as motion estimation, motion compensation, inter-frame prediction, quantization, perceptual weighting, entropy coding, variable encoding, and motion vectors, as well as for decoding operations such as reverse operation between different color formats (YIQ, YUV, and RGB) for display purposes. DCTs are also commonly used for HDTV (High Definition Television) encoder/decoder chips. [23] As we know, images are represented in rows and columns, we have the following syntax in which images are represented: Fig. 20.7. Section of the CAS object after preprocessing the image. Here are some basic functions that can be performed on an image, which changes the properties of the image. Digital filters are used to blur and refine digital images.

In 1972, the engineer of the British company EMI Housfield invented the X-ray computed tomography device for the diagnosis of the head, which we usually call CT (computed tomography). The CT kernel method is based on the projection of the human headboard and is computer-assisted to reconstruct the cross-sectional image, called image reconstruction. In 1975, EMI successfully developed a whole-body computed tomography machine, which received a clear tomographic image of various parts of the human body. In 1979, this diagnostic technique won the Nobel Prize. [4] Digital image processing technology for medical applications was inducted into the Space Foundation Space Technology Hall of Fame in 1994. [24] Image processing plays a key role in many industries such as medicine, computer vision and AI. Clinical imaging is the method used in the medical field to diagnose or study the disease by taking pictures of parts of the body. Millions of image tests are performed worldwide for research purposes. Advanced image processing techniques, including image retrieval, analysis and enhancement, have greatly contributed to the development of medical imaging. In computer vision and AI, image processing techniques are used to extract hidden features from the image.

This is very effective in processes such as image recognition and facial recognition. Apart from that, image processing can be used to restore and fill in the missing or damaged parts of an image. This includes the use of image processing systems that have been extensively trained with existing photo recordings to create new versions of old and damaged photos. Image processing is the process of converting an image into a digital form and performing certain operations to obtain useful information. The image processing system typically treats all images as 2D signals when certain predetermined signal processing methods are used. The image enhancement feature uses its algorithms to enhance image properties. It adjusts the image in such a form that all the results are more suitable for further analysis. It also helps the user to extract hidden features using techniques such as sharpening, curves, etc. The function improves viewing, removes unwanted parts, blurs the image and much more. As soon as the image has been captured by digital media, it is introduced into the image processing module.

Preprocessing of the captured image is performed to improve the resolution, noise, and color of the image. Segmentation is performed for the extended image and the segments of the image are referenced to the associated images stored in the database. Segmentation makes it possible to detect whether it is a plant, a soil or residues. Using the reference image, the processor compares it to the segmented image and detects if there is a disease or defect in the system. The charge-coupled device was invented by Willard S. Boyle and George E. Smith at Bell Labs in 1969. [7] While researching MOS technology, they realized that an electric charge was the analogy with the magnetic bubble and that it could be stored on a tiny MOS capacitor.

Since it was quite difficult to make a series of MOS capacitors in a row, they connected them with a suitable voltage so that the charge could be transmitted from one to the other. [5] CCD is a semiconductor circuit that was later used in early digital video cameras for television transmission. [8] In the image above, an image was taken by a camera and sent to a digital system to remove all other details and simply focus on the drop of water by zooming it in so that the image quality remains the same. Wavelets are used to display images in different degrees of resolution. Images are divided into wavelets or smaller areas for data compression and pyramid representation. Fig: Reconstruction of damaged images using image processing (Source) The course also has a project component in which students are encouraged to study image processing applications in their respective fields. Students receive comprehensive imaging equipment such as digital camcorders and Firewire cards to download digital videos to computers, webcams, state-of-the-art video conferencing equipment, and video editing software to develop their projects. Towards the end of the semester, students present their work and are evaluated by the whole class based on creativity, difficulty of the problem and presentation.

Based on our experience and student feedback, we found that the project component proved to be extremely effective in motivating and maintaining students` interests in the general field of image processing. Students love to discover image processing applications in their respective fields and to our pleasant surprise, the winners of the «Ram`s Horn Best Project Award» are often students with a non-technical background!1 To apply the affine matrix to an image, the image is converted into a matrix in which each input corresponds to the intensity of the pixels at that time. Then, the position of each pixel can be represented as a vector indicating the coordinates of that pixel in the image, [x, y], where x and y are the row and column of a pixel in the image matrix. This multiplies the coordinate by an affine transformation matrix that specifies where the pixel value is copied to the output image. Compression is a process used to reduce the memory needed to store an image or the bandwidth required for transmission. This happens especially if the image is intended for use on the Internet. The Laplacian is a second derived filter used to find the edges of an image. The Laplacian image highlights a rapid change in intensity in the images; These areas are usually the margins. This type of filter is prone to noise, so to apply this filter, you need to smooth the image. In most cases, the image is smoothed with the Gaussian filter, and then the Laplacian filter is applied.

Image recovery is the process of improving the appearance of an image. However, unlike image enhancement, image recovery is performed using certain mathematical or probabilistic models. A knowledge-based segmentation system for texture images was proposed in [42-44]. This system is characterized by a control mechanism based on an iterative Linked Quadtree Splitting (ILQS) scheme. The main advantages of the ILQS scheme are that model matching assumes that a seismic model can be represented by a series of matrices called models. Each seismic region corresponds to a seismic model described by a series of models. These models can be selected by an expert from an already interpreted seismic section. Another matrix (with the same dimensions as the template) contains the reflection coefficients around a pixel of the seismic image [38]. The projection vectors of this matrix on stencils and projection angles can be used to classify a seismic image pixel in an area.

The classification of a pixel in relation to an area can be based either on the largest projection standard or on the smallest projection angle. Segmentation of model matching can be followed by relaxation labeling techniques to reduce the risk of misclassification of pixels in a seismic image [30].