High-quality imaging under low scattering conditions using the light field contribution matrix model

The prospective aspects of the application of digital image processing in the field of monitoring the coking process inside the boilers in thermal power plants are discussed. Timely monitoring the dynamics of the coking is an important issue to the safety of thermal power plants. Digital image processing is an effective technique to fulfill this monitoring. Edge detection and wavelet transform serve as accurate tools to investigate the ash images captured by the high-speed camera to inspect the coking. Edge detection finally presents the boundary contour of the coking zone. The advantage of wavelet transform over Fourier transform is that the former obtains not only the information on the frequency but also the information on time.

This work investigates digital image processing technology and presents a super-resolution image reconstruction approach to increase the logic of urban and rural planning spatial layout. Furthermore, this work integrates GIS technology with super-resolution image reconstruction techniques to create a digital image processing and GIS-based urban and rural spatial layout design model. In addition, this paper mathematically expresses the objects and scenes that need to be simulated as a collection of three-dimensional graphic objects stored in the computer. The platform is constructed with a three-tier system of B/S architecture, and the logical structure of the entire system is composed of the client, application server, database server, and other functional servers. Furthermore, this paper constructs the functional structure of urban and rural spatial layout planning model based on digital image processing and GIS technology and verifies the model in this paper through experimental research. The experimental statistical results verify the reasonableness of the model in this paper.

With the development of electronic technology, computer software and hardware technology, image processing and technology related to human vision, the technology of machine vision image processing has also achieved rapid development. With the rapid development of science and technology, digital image processing technology has been widely used in various fields, among which image segmentation is an extremely important technology. Image segmentation is an important intermediate technology in digital image processing. It relies on the underlying technology of image digital processing to serve high-level applications such as pattern recognition. Processing the information interaction between machine and the real world is the first step in the study of artificial intelligence, and the goal of computer vision research is to make the computer have the ability to perceive and understand image information similar to human. This paper expounds the basic principle of image segmentation technology, analyzes and discusses the methods of image segmentation, and studies the application of image segmentation technology in digital image processing.

In recent years, the level of science and technology in China has been significantly improved. Digital image processing technology is the product of the times with the rapid development of science and technology, and has been applied in many fields. Digital image processing technology includes many practical technologies, and image segmentation technology is one of them. At present, the widely used pattern recognition technology is realized by image segmentation technology. Based on this, this paper mainly studies the application of image segmentation technology in digital image processing. This paper first introduces the methods of image segmentation, including threshold segmentation, clustering segmentation and edge detection segmentation. Secondly, according to a need to extract a road image for image segmentation, and achieved very good results. Finally, this paper mainly analyzes the application of image segmentation in digital image processing. It is found that image segmentation has good application in automatic license plate recognition, biomedical engineering, remote sensing engineering and fire prevention and detection.

Continuous time filter research has aided in the development of digital filters. To ensure that this chapter is comprehensive, the authors begin by providing an overview of continuous filter synthesis, which is accomplished using analogue components such as resistors, inductors, condensers, and even active components. This chapter establishes a connection between the theoretical introduction of various digital filters and their application in digital image processing for a variety of purposes. Authors will discuss stopband, passband, Butterworth filter, finite impulse filter and infinite impulse filter in this chapter. With numerous applications of digital image filters such as mean, median, Gaussian smoothing, conservation, Crimmins speckle removal and frequency filters.

Image restoration is the process of obtaining a distorted/noise image and giving an approximate clear image of the original image. False focus, motion blur and noise are forms of distortion. Image restoration can be done by reversing the process called Point Extension Function (PSF). In this process, the blurred image is generated by point source imaging and can be used to restore the image lost due to the blur process. Like to form. Modern artificial intelligence (AI) applied to image processing includes facial recognition, object recognition and detection, video, image action, and visual search. It helps to develop smart applications in digital image processing.

Patch-based techniques are proven to generate promising results and outperform many of the existing state-of-art techniques for most of the applications in digital image processing. In this work we develop a patch based coherent texture synthesis technique. A patch-based anisotropic diffusion techniques combined with a novel high-frequency generating technique that can enhance line/brush strokes is also proposed. Though these techniques can be applied to many different image processing applications, here we have limited ourselves in the application to interactive digital mural painting recovery/restoration. Some empirical and practical evidence for its high quality texture synthesis and restoration for mural paintings are also presented. The experimental results show the effectiveness of the proposed algorithm.

Banana plants (Musa spp.) are one of the leading horticultural commodities in Indonesia that have high economic value and play an important role in national food security. However, banana productivity often decreases due to attacks by various diseases such as Sigatoka, Cordana, and Pestalotiopsis infections that can spread quickly. Early detection of these diseases is crucial to prevent greater losses. This study aims to develop a banana plant disease detection system based on digital image processing with the Support Vector Machine (SVM) algorithm. The research method includes the stages of banana leaf image acquisition, pre-processing using color segmentation, color and texture feature extraction, and disease type classification with the SVM algorithm. The test results show that the developed system is able to recognize banana leaf diseases with an accuracy of 97.8%, precision of 97%, and recall of 98%. These findings prove that the application of digital image processing and the SVM algorithm is effective in detecting banana plant diseases. This system is expected to be a fast, efficient, and accurate diagnostic tool for farmers to increase the productivity and quality of banana harvests.

The Remote Sensing Image Scene Classification (RSI-SC) is a fundamental and challenging task in a remote sensing community area and used for earth observation application such as agricultural monitoring, civilian monitoring in military, city planning, crop planning, geomorphology, Land Use and Land Cover (LULC), metrology and soil mapping. In remote sensing image analysis, satellite and drone-captured images are used to study the Earth’s surface. The goal of image classification systems in this field is to assign meaningful labels to captured images, allowing them to be organized in a semantic structure. Such semantic ordering of images has wide applications in digital image processing and computer vision, including remote sensing, image retrieval, object recognition, image annotation, scene analysis, content-based image analysis, and video analysis. Feature extraction and representation at lower and intermediate levels was the mainstay of earlier approaches to remote sensing image analysis. The use of smaller image datasets hindered these approaches, but their great performance was demonstrated by combining numerous features and applying machine learning techniques. There has been a recent trend toward using deep learning models for remote sensing picture analysis, with hybrid deep learning approaches outperforming single models. This review article provides an in-depth examination of current deep learning methodologies, including low- and mid-level feature representation. It also provides a synopsis of image standards that are available to the public for use in remote sensing image analysis. We also compare different techniques that represent recent advances in deep learning models, addressed the challenges of remote sensing images and discussed about current applications in the field.

In this paper, we proposed a novel application based on digital image processing model in Freight Train Inspection (FTI) system. Each wagon image of freight train is captured by enterprise Automated Train Scanning System (ATSS). The main challenge of this application is automatically detecting the appropriate region of interest on the image for inspection. To address this problem, the improved adaptive multi-scaled retinex (MSR) and K-mean clustering methods are applied for image enhancement and positioning techniques. In the experimental the proposed model were found to improve the segmentation efficiency, thereby the result of inspection is optimized. The evaluation result also demonstrated the good system performance when the application is implemented using Vision Services Oriented (VSO) framework.

Handwritten digit recognition using the MNIST dataset is one of the applications in digital image processing. The selection of hyperparameters in the CNN architecture for handwriting recognition presents a challenge in achieving better recognition accuracy. This research focuses on the implementation of the DenseNet-201 architecture for recognizing handwritten digits in the MNIST dataset. The research stages include dataset preprocessing, model training, model testing, and model evaluation. The MNIST dataset consists of 60,000 training data and 10,000 testing data. Dataset preprocessing involves resizing the images to a larger size. The model training applies the DenseNet-201 architecture with selected hyperparameters such as activation functions (Softmax and ReLU), optimizers (Adam, RMSprop, and SGD), and learning rates (0.1, 0.01, and 0.001). The model testing uses one of the nine best-performing trained models. Model evaluation uses a confusion matrix to assess the accuracy and recognition performance on the MNIST dataset. The results show that the DenseNet-201 architecture with the RMSprop optimizer and a learning rate of 0.001 achieved a handwritten digit recognition accuracy of 99.49%. This study provides insights into CNN architectures and optimal hyperparameter selection for digital image processing

We present a detailed analysis of the Jacobi-Fourier moments and their applications in digital image processing. In order to reach numerical stability during the computation of the Jacobi radial polynomials a recursive approach is described. Also, some dis...

Orthogonal rotation-invariant moments (ORIMs), such as Zernike moments, are introduced and defined on a continuous unit disk and have been proven powerful tools in optics applications. These moments have also been digitized for applications in digital image processing. Unfortunately, digitization compromises the orthogonality of the moments and, therefore, digital ORIMs are incapable of representing subtle details in images and cannot accurately reconstruct images. Typical approaches to alleviate the digitization artifact can be divided into two categories: 1) careful selection of a set of pixels as close approximation to the unit disk and using numerical integration to determine the ORIM values, and 2) representing pixels using circular shapes such that they resemble that of the unit disk and then calculating ORIMs in polar space. These improvements still fall short of preserving the orthogonality of the ORIMs. In this paper, in contrast to the previous methods, we propose a different approach of using numerical optimization techniques to improve the orthogonality. We prove that with the improved orthogonality, image reconstruction becomes more accurate. Our simulation results also show that the optimized digital ORIMs can accurately reconstruct images and can represent subtle image details.

Application of digital image processing for the generation of voxels phantoms for Monte Carlo simulation

SEM temperature study of magnetic domain structure in cobalt monocrystals