Image Processing Applications Research Articles

Charting New Frontiers: Insights and Future Directions in ML and DL for Image Processing

The Special Issue “Deep and Machine Learning for Image Processing: Medical and Non-medical Applications” of the MDPI journal Electronics marks a pivotal point in the exploration of machine learning (ML) and deep learning (DL) applications in image processing [...]

Towards constructing a DOE-based practical optical neural system for ship recognition in remote sensing images

The optical neural network system based on the 4f system (4f-ONN) is a feasible solution for on-orbit real-time target detection and recognition on remote sensing images, as it can directly modulate and encode the two-dimensional image information. However, traditional 4f systems based on spatial light modulators (SLMs) often encounter misalignment errors during assembly due to the reflective optical path used in SLMs. Additionally, implementing electronic SLM in space-based applications introduces problems such as particle number reversal, significantly reducing the reliability of the system. To address these issues, this paper proposes the adoption of diffractive optical elements (DOEs) to construct the 4f-ONN system. The DOE-based design offers a more compact structure, enhanced reliability, and reduced energy consumption, making it highly suitable for on-orbit image processing applications. Due to the expensive and time-consuming nature of the DOE manufacturing process, a design approach for a 4f system based on DOE was pursued through software simulation in this paper. The simulation phase involved the utilization of electronic neural networks to acquire the physical parameters of the DOE mask, while incorporating the array theorem and Fraunhofer diffraction theorem to accurately calculate the physical dimensions of the DOE. The effectiveness of the adopted DOE-based 4f system was initially validated through experiments on a simple pattern dataset. Subsequently, simulation experiments were conducted on three public datasets, namely Mnist, Fashion-mnist, and QuickDraw16, to confirm the efficacy of the DOE-based 4f system in classification tasks. Lastly, target recognition experiments were performed on the GF-2 dataset, and a corresponding hardware system was developed to demonstrate the potential of the DOE-based 4f system in on-orbit image processing.

Image compression and reconstruction using improved Stockwell transform for quality enhancement

Image compression is an important stage in picture processing since it reduces the data extent and promptness of image diffusion and storage, whereas image reconstruction helps to recover the original information that was communicated. Wavelets are commonly cited as a novel technique for image compression, although the production of waves proceeding smooth areas with the image remains unsatisfactory. Stockwell transformations have been recently entered the arena for image compression and reconstruction operations. As a result, a new technique for image compression based on the improved Stockwell transform is proposed. The discrete cosine transforms, which involves bandwidth partitioning is also investigated in this work to verify its experimental results. Wavelet-based techniques such as multilevel Haar wavelet, generic multiwavelet transform, Shearlet transform, and Stockwell transforms were examined in this paper. The MATLAB technical computing language is utilized in this work to implement the existing approaches as well as the suggested improved Stockwell transform. The standard images mostly used in digital image processing applications, such as Lena, Cameraman and Barbara are investigated in this work. To evaluate the approaches, quality constraints such as mean square error (MSE), normalized cross-correlation (NCC), structural content (SC), peak noise ratio, average difference (AD), normalized absolute error (NAE) and maximum difference are computed and provided in tabular and graphical representations.

Development of Customized MIPS_32 Core Processor for Image Processing Applications

Definitely, image processing operations without advanced and expensive microprocessors consume more time, power, and larger programs. So, improving the reasonable cost of microprocessors is crucial in this situation. This paper proposes an improvement for the MIPS_32 architecture that is called a Customized MIPS_32 (CMIPS_32) to enhance the capabilities of image processing (IP) operations. The proposal aims to increase throughput by minimizing the iterative fetching of instructions required by a certain IP operation into a single customized IP instruction. The architecture of MIPS_32 was developed in two phases. Firstly, the Register File, control unit, and ALU are modified to manipulate the information related to the IP operations. Secondly, two new units, the address calculation unit and the last pixel detection unit, were proposed to determine a certain image's starting and ending addresses. Furthermore, the MIPS_32 pipeline is customized to have five to six stages depending on the intensity of operation required by a certain IP instruction to decrease the number of machine clocks and the power consumed. The proposal was implemented using the Zed-Board XC7Z020CLG484-1 FPGA. The results showed that the computation speedup increased by a factor equal to the number of standard instructions required to execute the same operation performed by one of the proposed IP instructions. The CMIPS_32 consumed less power than other models that were implemented on Spartan3-XC3S1500L, Virtex5-XC5VFX30T, Virtex6-XC6VLX75T, and Virtex6-Low-Power-XC6VLX75T by 0.0138W, 0.6468W, 1.31W, and 0.7898W, respectively. Comparing the power consumed by the proposal with the GPU proved that the CMIPS_32 consumes less than the NVIDIA-GPU-GTX980 by 63.8698W. Index Terms— FPGA, Image processing instructions, MIPS_32, Verilog.

FPGA-Based Satellite Vision Systems using Verilog HDL

Abstract: It is believed that the most effective way to collect information about the Earth's surface is through high- quality satellite images. Extracting a feature from an image is really difficult because you have to choose the best image segmentation methods and combine many strategies to find the Region in the most effective manner. This study makes recommendations for the classification techniques for objects in the satellite. On high-resolution satellite images, applying image processing methods. The methods used to define region mostly focus on urban, agricultural, and forest regions. There are several methods for extracting these traits. Using a Grey Level Cooccurrence Matrix is the most used method. It is employed to unveil specific characteristics regarding the spatial arrangement of gray levels in the texture image. The Grey Level Co-occurrence Matrix (GLCM) captures statistical details of neighboring pixels in an image, enabling the computation of textural features that enhance the comprehension of visual content. This research presents a VLSI implementation aimed at extracting four texture characteristics from the grey level co-occurrence matrix. Verilog was employed to model the hardware, with MATLAB used for software simulation. The simulation utilized the Verilog HDL language from the XILINX tool, and the implementation was executed on the SPARTAN FPGA board.

MMDCP: An Image Enhancement Algorithm Incorporating Multi-Channel Phase Activation and Multi-Constrained Dark Channel Prior

The quality of visual media is critically impacted by low illumination and the presence of airborne particulates, leading to challenges in brightness balance, color saturation, and texture clarity which are detrimental to various applications in image processing and computer vision. Addressing these challenges, this study introduces a novel image enhancement algorithm that significantly improves the quality of degraded images. Our proposed method, the multi-channel phase activation and multi-constraint dark channel prior (MMDCP), leverages an innovative approach by integrating the phase-adjusted Gaussian kernel function for brightness channel optimization in the Fourier transform frequency domain. This optimization is enhanced through the application of a saturated dark channel prior, achieving simultaneous brightness enhancement and color fidelity. Furthermore, we refine the dark channel prior deblurring algorithm by incorporating intensity, brightness, and color constraints to correct overexposure issues and color offsets in the reconstructed images. The efficacy of the MMDCP algorithm is demonstrated through extensive experimentation, comparing it against six contemporary image enhancement algorithms using two types of objective indicators and subjective assessments across four public datasets. The MMDCP algorithm consistently outperforms the existing methods, with a notable average improvement of 20% in PSNR and 19.6% in SSIM metrics, substantiating its superiority in enhancing brightness, detail, and color accuracy. This study’s results underline the MMDCP algorithm’s robustness and versatility in improving image quality across various conditions, including daytime, nighttime, indoor, and outdoor settings.

Enhance watershed algorithms using principal component analysis capabilities

Traditional image segmentation algorithms have many drawbacks, such as over-segmentation and image distortion due to reflected light. The Watershed algorithm is one of the most popular image segmentation algorithms. Over-segmentation errors caused by overlapping targets in the image, as well as noise and glare, must be removed. In this article, we apply image processing using the watershed algorithm and propose to improve the algorithm based on principal component analysis. PCA is a popular technique for analyzing large datasets with many advantages per observation. PCA improves data interpretability while maximizing information content, enabling visualization of multidimensional data by finding image component gradients in a new space called the principle component that is unaffected by noise and reflected light. In contrast, the components mainly containing noise will eliminate with negligible information. This paper introduces three primary steps. The process involves applying the watershed algorithm to the image in the first phase, using the proposed approach (applying the watershed algorithm and suggesting an improvement based on principal component analysis) to the image in the second step, and comparing the outcomes of the two previous processes. Test results show that the suggested technique can achieve accurate and durable target shapes.

Exploring the significance and applications of Field Programmable Gate Arrays in modern integrated circuits

In recent years, the rapid advancement of electronic technology and large-scale integrated circuit technology has led to increased integration of integrated circuits and expanding system scales on integrated motherboards. This evolution presents new challenges and demands in system design. Field Programmable Gate Arrays (FPGAs), a vital VLSI technology, have found extensive applications in communication, image processing, computers, and other domains, becoming a pivotal component of contemporary electronic systems. This paper aims to enhance the understanding of FPGAs by first delving into their theoretical foundations, followed by an overview of their general structural elements and a historical perspective on FPGA development. Additionally, this analyse and summarize relevant literature in the third section, focusing on the three primary application areas of FPGAs, elucidating their research processes and findings. Finally, the fourth section offers insights into the future directions of FPGA development, grounded in the current context. By acquainting readers with FPGAs essential attributes and its multifaceted applications, this paper underscores the pivotal role of FPGAs in the landscape of modern integrated circuits.

Application of Hybrid Image Processing Based on Artificial Intelligence in Interactive English Teaching

Primary school English teaching resources play an important role in primary school English teaching. The information age requires that primary school English teaching should strengthen the use of multimedia resources and gradually realize the diversification of teaching content. Expanded reality innovation is a sort of mixture picture handling innovation, which is one of the significant innovations that would influence the improvement of fundamental schooling in the following five years. It can seamlessly output virtual objects to the real environment, which is convenient for this paper to obtain and absorb information. It can also help students to participate in exploration and cultivate their creativity and imagination. It can strengthen the cooperation between students and teachers and create various learning environments. It has an immeasurable prospect of development in the field of education. The primary school English teaching resources based on augmented reality create a realistic learning situation from two-dimensional plane to three-dimensional three-dimensional display, and enrich the presentation of primary school English teaching content. It can stimulate students’ interest in learning English and promote the transformation of English teaching methods. It is a useful attempt in the field of education. This paper made statistics on the test results of the experimental class and the control class. Most of the scores of the experimental group were between 71 and 100, a total of 27, accounting for 67.5%. The score distribution of the control class was relatively balanced, with the highest number between 61-70, and the number was 10, accounting for 25%. Therefore, it can be seen that hybrid image processing technology is important for interactive English teaching.

Fresnel–Arago laws: an easily feasible demonstration and laboratory exercise for students

The basic premise of this work was to create a simple experimental setup for the demonstration of Fresnel–Arago laws and the realization of an experimental exercise. An Iceland calcite is used as a beam splitter, and by adding a diverging lens (commonly used to magnify the image), a large image of overlapping spots is obtained at a short distance, visible to a wide audience. Additionally, by placing a diverging lens at different distances from the calcite, the level of the spots overlapping can be changed. The interference image is photographed by a smartphone and processed by one of the available applications for quantitative image processing. The experiment was carried out using red and green laser light. The same setup, with a diverging lens, also allows the reconstruction of the original Fresnel and Arago experiment with a thin metal cylinder. Quick and easy construction, relatively simple processing of the interference image, without the use of expensive devices (CCD camera, beam splitter, beam expander), and the good agreement between the nominal and measured laser light wavelengths, make this setup very suitable for realization as a student exercise for undergraduate and master’s students.

Estimation of total forces of jets on liquid interfaces using image processing methods

Optical imaging is a fundamental method for detecting plasma phenomena, such as plasma bubbles, plasma streamers, and self-organized plasma patterns. Therefore, data mining of images is crucial in plasma diagnostics. This work presents image processing methods for extracting substructures in plasma bubbles, streamers, and patterns. In our experiment, plasma–liquid interfaces between atmospheric pressure argon-plasma jets and NaCl solutions are quantitatively captured. After extracting accurate plasma–liquid interfaces, traditional analytic functions and machine learning approaches are used to fit curves of interfaces. Regression of machine learning method based on Gaussian process reveals many details of interfaces, but neural networks present smooth and accurate regressions. The gravitational and surface tension forces are calculated using experimental plasma–liquid interfaces. The plasma forces are estimated to be several tens of Pa. However, they increased to several hundred Pa around the inflection points of interfaces. This study extends the application of image processing to plasma diagnostics and provides target data of gas–liquid interfaces for numerical simulations.

Improving Digital Satellite Image for security purposes

Satellite imagery is employed in many different fields of study. These pictures have serious quality problems. Image enhancement algorithms, however, can improve it in terms of contrast, brightness, feature elimination from noise contents, etc. These algorithms present and analyse the picture's properties by sharpening, focusing, or smoothing the image. Therefore, the specific application determines the goal of picture enhancement. This paper briefly overviews picture enhancement methods that produce optimal and progressive outcomes for satellite images used for secured remote sensing. To do this, various image enhancement techniques are used, which are widely used today to improve image quality across various image processing applications. Some commonly used image enhancement techniques include spatial filtering, contrast stretching, and histogram equalisation. These techniques aim to enhance the visual quality of satellite images by adjusting brightness and contrast and reducing noise. These methods can also improve the interpretability of the images for remote sensing purposes. The enhancement of satellite images finds use in several fields, particularly security. It is essential for security applications, including threat detection, border control, and surveillance. Security professionals may more effectively analyse and understand data to spot any dangers or questionable activity by boosting the visual details and general quality. Keywords: Satellite image analysis; mean filter; secured application; SVM; wavelet transformation

An Integrated Thresholding and Morphological Process with Histogram-based Method for Brain Tumor Analysis and MRI Tumor Detection

INTRODUCTION: Over the past several years analysis of image has moved from larger system to pervasive portable devices. For example, in pervasive biomedical systems like PACS-Picture achieving and Communication system, computing is the main element. Image processing application for biomedical diagnosis needs efficient and fast algorithms and architecture for their functionality. Future pervasive systems designed for biomedical application should provide computational efficiency and portability. The discrete wavelet transform (DWT) designed in on-chip been used in several applications like data, audio signal processing and machine learning.
 OBJECTIVES: The conventional convolution based scheme is easy to implement but occupies more memory , power and delay. The conventional lifting based architecture has multiplier blocks which increase the critical delay. Designing the wavelet transform without multiplier is a effective task especially for the 2-D image analysis. Without multiplier Daubechies wavelet implementation in forward and inverse transforms may find efficient. The objective of the work is on obtaining low power and less delay architecture.
 METHODS: The proposed lifting scheme for two dimensional architecture reduces critical path through multiplier less and provides low power, area and high throughput. The proposed multiplier is delay efficient.
 RESULTS: The architecture is Multiplier less in the predict and update stage and the implementation carried out in FPGA by the use of Quartus II 9.1 and it is found that there is reduction in consumption of power at approximately 56%. There is reduction in delay due to multiplier less architecture.
 CONCLUSION: multiplier less architecture provides less delay and low power. The power observed is in milliwatts and suitable for high speed application due to low critical path delay.

The Enhancing Accuracy in Mycobacterium Tuberculosis Counting on Acid-Fast Bacteria Sputum through Microscope Camera and Image Processing with Thresholding Method

Tuberculosis (TB) is a chronic infectious illness. Pulmonary TB is still a global threat and concern. Currently, TB is among the top 10 causes of death in the world in 2015 and it is estimated that there are around 10.4 million new cases of TB in the world. 6 countries account for around 60% of new cases in the world: India, Indonesia, China, Nigeria, Pakistan, and South Africa. Globally, the TB death rate was around 22% of total world deaths from 2000 to 2015. This study introduces an innovative tool, the "Microscope Camera for Enhanced Mycobacterium Tuberculosis Counting in Acid-Fast Bacteria Sputum through Image Processing with Thresholding Method," designed to revolutionize the examination of tuberculosis (TB) samples. In response to the global threat of Pulmonary Tuberculosis, the tool employs a state-of-the-art CMOS-HD (Complementary metal-oxide-semiconductor High Definition) camera module and leverages the Python application for efficient image processing. The primary objective is to address the limitations faced by conventional diagnostic methods, particularly the challenges associated with the Rapid Molecular Test Method due to its dependence on expensive equipment, thereby making it less feasible for implementation in resource-constrained public health centers. The study underscores the persistent threat of TB as a global health concern, emphasizing its prevalence, with approximately 10.4 million new cases worldwide. Focusing on Ziehl-Neelsen stain sputum samples, the research seeks to elevate the accuracy of TB diagnosis by proposing a streamlined approach. This involves integrating a digital microscope system with a CMOS-HD camera module and utilizing advanced image processing techniques, specifically the Python Thresholding method. In the methodology, the research outlines a comprehensive data collection and analysis process. Mechanical and block diagrams illustrate the design of the module, while the system flow diagram elucidates the sequence of steps involved in TB sample analysis. The data analysis section employs metrics such as average, percentage error, and success percentage, providing a quantitative assessment of the system's performance. The results reveal an encouraging average system accuracy of 85.30%, demonstrating the potential of the developed tool to enhance the diagnostic process. However, the study transparently acknowledges areas for improvement, particularly in addressing discrepancies between manual and system calculations. The conclusion emphasizes the tool's promising role in assisting healthcare workers in detecting and counting TB bacteria in sputum, underscoring the need for ongoing refinements to achieve a definitive 100% accuracy and eliminate the possibility of misdiagnoses.

Artificial intelligence and skin cancer.

Artificial intelligence is poised to rapidly reshape many fields, including that of skin cancer screening and diagnosis, both as a disruptive and assistive technology. Together with the collection and availability of large medical data sets, artificial intelligence will become a powerful tool that can be leveraged by physicians in their diagnoses and treatment plans for patients. This comprehensive review focuses on current progress toward AI applications for patients, primary care providers, dermatologists, and dermatopathologists, explores the diverse applications of image and molecular processing for skin cancer, and highlights AI's potential for patient self-screening and improving diagnostic accuracy for non-dermatologists. We additionally delve into the challenges and barriers to clinical implementation, paths forward for implementation and areas of active research.

Edge Detection on Field Programmable Gate Array (FPGA)

Edges are significant local changes in the image and are very important features for analysing images. Edges typically occur on the boundary between two different regions in an image. An edge in an image is a significant local change in the image intensity, usually associated with a discontinuity in either the image intensity or the first derivative of the image intensity. Edge detection is one of the tools that can be used in many image processing applications for withdrawing information from image. Sobel edge detection is one of the operators that operate on gradient based edge selection methods to find edge pixels in the image. This report proposed an implementation of Sobel edge Detection algorithm to find the pixels in grayscale images. We present a video streaming architecture and IP implementation using high level synthesis. A video with 720p resolution streamed from the HDMI source and real time edge detected video captured on another monitor. For implementation, ZYBO board or kit is used with Vivado software with 2018.3 V, which has provided adequate peripherals for implementation.

Design and implementation of hybrid (radix-8 Booth and TRAM) approximate multiplier using 15-4 approximate compressors for image processing application

Design and implementation of hybrid (radix-8 Booth and TRAM) approximate multiplier using 15-4 approximate compressors for image processing application

Noise Removal from the Image Using Convolutional Neural Networks-Based Denoising Auto Encoder

With the exponential growth in the volume of digital images captured daily, there is an escalating demand for elevating image quality to achieve both accuracy and visual appeal. Addressing this need, the development of techniques for reducing image noise while preserving crucial features, such as edges, corners, and sharp structures, has become imperative. This paper delves into the significance of image denoising and introduces a novel approach utilizing a denoising autoencoder based on convolutional neural networks (CNNs). The proposed method adopts a meticulous two-step process to effectively eliminate noise. Initially, input images are segregated into training and testing sets. Subsequently, a denoising autoencoder model is trained using the designated training data. This model is then further refined through training on a CNN, enhancing its noise reduction capabilities. The evaluation of the system's performance is conducted using testing data to gauge its effectiveness. The study employs the MATLAB programming language for implementation and evaluation. Results, measured through RMSE (Root Mean Square Error) and PSNR (Peak Signal-to-Noise Ratio) criteria on two distinct datasets—the Covid19-radiography-database and SIIM-medical-images—reveal that our proposed method outperforms existing approaches significantly. This approach is particularly promising for applications demanding enhanced image quality, such as the resolution enhancement of medical images. The study contributes to the ongoing efforts in noise reduction research, offering a robust solution for improving visual perception in diverse image processing applications.

Image Processing Using High‐Index Dielectric Metasurfaces Based on Fano Resonance

AbstractThis paper presents the design and analysis of high‐index dielectric metasurfaces based on Fano resonance for image processing applications. With the aim of optical edge detection, two different metasurface structures are proposed to create first‐ and second‐order derivative operators. The metasurfaces designed for second‐order derivative is a square lattice structure consisting of silicon cylinders surrounded by a silicon dioxide layer. Moreover, another metasurface with a square lattice structure composed of three different silicon cylinders embedded in a silicon dioxide layer is proposed to perform first‐order derivation. To achieve the best performance of the proposed metasurfaces in edge detection, the physical dimensions of the proposed structures are optimized by conducting parametric studies to reach the maximum available numerical aperture . Consequently, the second‐order derivative metasurface attains a numerical aperture with a magnitude , while the first‐order derivative metasurface achieves a numerical aperture of . Finally, the applicability of the proposed structures in optical edge detection is verified in the analog domain. The proposed metasurfaces provide high spatial resolution, high efficiency, and low dependence on the horizon angle, which pave the way for high‐quality image processing applications.

Stochastic alternating structure-adapted proximal gradient descent method with variance reduction for nonconvex nonsmooth optimization

The blocky optimization has gained a significant amount of attention in far-reaching practical applications. Following the recent work (M. Nikolova and P. Tan [SIAM J. Optim. 29 (2019), pp. 2053–2078]) on solving a class of nonconvex nonsmooth optimization, we develop a stochastic alternating structure-adapted proximal (s-ASAP) gradient descent method for solving blocky optimization problems. By deploying some state-of-the-art variance reduced gradient estimators (rather than full gradient) in stochastic optimization, the s-ASAP method is applicable to nonconvex optimization whose objective is the sum of a nonsmooth data-fitting term and a finite number of differentiable functions. The sublinear convergence rate of s-ASAP is built upon the proximal point algorithmic framework, whilst the linear convergence rate of s-ASAP is achieved under the error bound condition. Furthermore, the convergence of the sequence produced by s-ASAP is established under the Kurdyka-Łojasiewicz property. Preliminary numerical simulations on some image processing applications demonstrate the compelling performance of the proposed method.