COMPARATIVE ANALYSIS STUDY OF BINARY IMAGE SMOOTHING TECHNIQUES

Programmable logic is emerging as an attractive solution for many digital image processing applications. As image sizes and bit depths grow larger, software has become less useful in the image processing, Field Programmable Gate Array (FPGA) technology has become a viable target for the implementation of algorithms suited to image processing applications, the unique architecture of the FPGA has allowed the technology to be used in many such applications encompassing all aspects of image processing. Image smoothing is one of image processing applications, it often done to reduce the effect of pixel noise in images. This paper presents VHDL architectures (that allow description of the structure design of FPGA) to implement two of image smoothing filters: a) averaging filters b) median filters This research is also applying the filters on FPGA. The results proves high-speed performance of the algorithms that rely on hardware and software compared to software alone, as it appeared very big difference in the speed of execution, depending on the hardware devices that showed the speed of the implementation of the scale of nanosecond, while the software application of algorithms is measured in seconds. The software was implemented in this research using MATLAB 2010 language code as well as the VHDL language to deal with use of FPGA device, which was of a kind (Xilinx XC3S500E Spartan-3E).

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.

The current development of digital image processing techniques have been very rapid. Application of digital image processing both hardware and software are available with a variety of features as a form of superiority. Medical ultrasonography is one of the results of digital image processing technology. It is a kind of diagnostic imaging technique with ultrasonic that is used to produce images of internal organs and muscles, size, structure, and wound pathology, which makes this technique is useful for checking organ. However the images produced by low resolution ultrasonography device is not fully produce clear information. In this research we use histogram equalization to improve image quality. In this paper we emphasize on the comparison of the two methods in the histogram equalization, namely Enhance Contrast Using Histogram Equalization (ECHE) and Contrast-Limited Adaptive Histogram Equalization (CLAHE). The results showed that CLAHE give the best results, with the parameter value Nbins 256 and Distribution Rayleigh with MSE value 9744.80 and PSNR value 8.284150.

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.

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.

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.

A procedure for digital image correlation is described which is based on least squares window matching. The immediate aim is high precision parallax assessment, point transfer, and point measurement. Experiments and theory have confirmed the high accuracy potential of the method. By implementation of charge coupled device (CCD) video cameras in an analytical plotter, an experimental hardware and software configuration has been established with which the operational on line application of digital image correlation for conventional photogrammetric measuring tasks can be tested. First results of calibration and performance of the system are presented. They allow optimistic conclusions as to the further development and practical application of digital image processing in photogrammetry.

A new application of digital image processing to investigate thin compressed films: The measurement of buckling propagation

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.

This paper concerns the application of digital image processing in combination with rulebased reasoning as a nondestructive quality testing method for resistance spot welding. The use of fuzzy logic gives the possibility to characterize colors by linguistic terms rather than by names for wavelength or wavelength intervals. The presented method supplements Lotfi Zadeh's idea of soft computing and computing with words. It can be applied to many problems in which color carries important information.

To have accurate runoff velocity, there is need to improve dye tracer method for estimating surface runoff velocity. This can enhance the calculations of relevant hydrologic parameters that will lead to a better understanding of hydrological processes and soil erosion. In this study, an integrated dye tracer and image processing method (IPV) and dye tracer method (AOV), respectively, were used to estimate runoff velocity under three slope gradients (5°, 10°, and 15°) and three slope positions (up-slope, mid-slope, and down-slope). The results showed more variation in runoff velocity under IPV than AOV. Both IPV and AOV were positively correlated with slope gradient. IPV values were close to AOV ones for slope gradients ≤5°, but were significantly different for slope gradients ≥10°. The mean AOV value was 10.6% higher than that of IPV. Regression analysis showed that compared with AOV, IPV overestimated and underestimated runoff under low and high runoff velocity conditions, respectively. The use of image processing in IPV was advantageous because of its ease of use with fewer artificial errors and its suitability for lateral diffusion of runoff. Irrespectively, additional studies are needed to verify and/or improve further the use of this method in runoff velocity analysis.

Application of digital image processing to a β-gauge for determining mass concentration of suspending particulate matter in atmosphere

The extraction of singly scattered photons is crucial for achieving high-quality imaging. Singular value decomposition of the optical transmission matrix (TM) can effectively separate singly and multiply scattered photons. However, traditional TM construction methods usually require capturing a large number of light field images, a process that is both time-consuming and complex, limiting its widespread use in practical applications. This paper introduces the light field contribution matrix (LFCM) model, which separates singly and multiply scattered photons using a single light field collection under low scattering conditions, enhancing measurement efficiency and enabling imaging in dynamic environments. Experimental results show that under low scattering conditions with a high proportion of singly scattered photons, the technique doubles the signal-to-noise ratio and increases the contrast by 1.5 times. In fly brain structure imaging experiments, LFCM successfully revealed hidden features and details, demonstrating its advantages in complex structural analysis. Additionally, since the technique is based on light field intensity information imaging, it has potential applications in digital image processing, particularly for reducing image blur caused by scattering, as demonstrated in the case of scatter blur correction of digital images of cataracts in the human eye. Therefore, this method provides a practical and efficient solution for high-quality imaging under low scattering conditions.

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.