Digital Image Processing Applications Research Articles

A fuzzy histogram weighting method for efficient image contrast enhancement

Image contrast enhancement is an important step in digital image processing applications. In this paper, we present an efficient contrast enhancement approach, which employs a histogram weighting method based on fuzzy system. It is able to enhance the contrast of input images while preserving their details. The proposed method divides the histogram of the original image into three sub-histograms using Fuzzy clustering. The obtained sub-histograms are weighted based on the Mamdani Fuzzy inference system, and then they are summed to generate a new histogram. The produced histogram is modified to reduce undesirable effects of its spikes and pits. Finally, the enhanced image is obtained by equalization of the modified histogram. The Mamdani fuzzy inference system assigns an appropriate dynamic range to each input interval of gray levels (sub-histogram), hence enhancing the image details. Experimental results for different types of images verified the merit of the proposed method in terms of preservation the input image details and improving its contrast.

A novel digital fuzzy system for image edge detection based on wrap-gate carbon nanotube transistors

This study presents a novel digital fuzzy approach for image edge detection based on carbon nanotube field effect transistor (CNTFET). The proposed design employs a simple fuzzy rule for digital image edge detection. To investigate the functionality and performance of the proposed fuzzy approach, our results are compared with the MATLAB edge detection operators such as Sobel, Canny, and Prewitt. Based on the simulation results, we demonstrate that the proposed fuzzy approach can be implemented through nanoscale logic gates and provide a reasonable accuracy. The core element of the proposed fuzzy system, implemented with CNTFETs, occupies 0.22 µm2 layout area and consumes 535 nW power consumption. Our results also emphasize the benefits of the Gate-All-Around (GAA) CNTFETs as a potential candidate for nanoscale digital fuzzy image processing applications.

Digital image analysis applied in asphalt mixtures for sieve size curve reconstruction and aggregate distribution homogeneity

Digital tools have been applied to study the mechanical and dynamic behavior of asphalt samples, allowing a rapid, simple, and economic analysis process. This work presents the results obtained from the application of digital image processing in asphalt mixtures. First, an algorithm for the digital reconstruction of the sieve size curve for different types of mixtures is developed. Then, with the sample image, the mixture segregation index (homogeneity in the distribution of the aggregates) is determined and this distribution is correlated with the maximum resistance and the work of fracture obtained with the Semicircular bend test (SCB). With images taken with a conventional camera of 18MP, an error of less than 4% is obtained in the reconstruction of the sieve size curve up sieve No.80. It is also evidenced that there is a correlation between the mixture segregation index within the image and the results of th e SCB test, presenting a higher incidence in asphalt mixtures with coarse aggregates. To corroborate the functionality of the m odel, the algorithm was applied for four types of hot mix asphalt (HMA) of different characteristics, such as an open-graded HMA, a dense-graded HMA, one with recycled pavement and the other made with natural asphalt.

Application of digital image processing for autofocusing system construction

The article states the method of autofocusing of the optoelectronic image, based on digital image processing. Some systems of autofocusing which can be used in optoelectronic equipment of unmanned aerial vehicles are considered. The method developed by authors and the program algorithm implementing it are similar to the rules of functioning of an element of the human visual system which executes an estimation of a spectrum of spatial frequencies for the realization of focusing of the image of objects which are of interest or danger and are on different distance from the observer. The best focusing is defined on the greatest width of a spectrum of spatial frequencies of the object. Such estimation allows focusing the necessary object on the observable image even in the absence of binocular vision. The method is based on the numerical analysis of a two-dimensional amplitude spectrum of the image. Results of operation of the stated method which confirm its efficiency are presented.

An Efficient VLSI Design of 32X32 bit Multiplier using Wallace Tree Algorithm in Vivado HLS and Xilinx ISE Software using VHDL

Multiplier is the most basic component present in any digital system. These multipliers are mainly used in Digital Signal and Image Processing applications. In applications like image detection latest sophisticated algorithms like CNN are used which contains MAC units in their design. The multiplier used in MAC unit requires huge memory, offers high latency and consumes more power. There are many algorithms such as Combinational, Sequential and Array Multiplication Algorithms which helps in designing Multiplier. The major drawback in all designs is circuit complexity. The problem of latency and power dissipation are also present. Considering all the drawbacks present in those algorithms this paper proposes the usage of Wallace Tree Algorithm which consumes less power and has low latency. Also, there are many ways to add the final stage of partial products generated such as Carry Look Ahead adder, Carry Select Adder etc. This paper uses both Carry Select Adder and Ripple Carry Adder for performing final addition of partial products. All previous partial products are added using Half adders and Full adders. The Multiplier is designed using VHDL in Xilinx ISE and Vivado Platform.

An efficient circuit for error reduction in logarithmic multiplication for filtering applications

SummaryReal‐time digital signal and image processing applications, such as filtering, demand high performance. Often, multiplication is one of the most time‐consuming steps of the filtering operation. Log‐based multipliers have been used for improving multiplication efficiency at the expense of accuracy. The objective of the proposed work is to improve the accuracy of log‐based hardware multipliers by appropriately altering the filter weights and without increasing the required resources.

A New Method for Designing Dense Skeleton Asphalt Mixture Based on Meso Parameter

At present, research on the internal structures of asphalt mixtures has mostly focused on the statistical analysis of their mesostructural components such as aggregates, voids, and asphalt mortars, in addition to the verification of the mechanical behaviour of the mixture through simulations. Furthermore, the capacity of the research has not risen to a level where a design method to guide the design and optimisation of the asphalt mixture gradation has been formulated. After an in‐depth analysis of the existing evaluation parameters and standards for the asphalt mixture skeleton, this study proposes a new method for precise designing a dense skeleton asphalt mixture (DSAM) based on meso parameter. The results indicate that the application of digital image processing (DIP) techniques to adjust the gradation increases the average coordination number () and reduces the ratio of the quantity of coarse aggregate without contact point to the total quantity of coarse aggregate (C value). This can effectively improve the meso parameters of the mixture so that the quality of the main skeleton is significantly enhanced; the process also has higher precision and demands less test work. VCAmix(IMAGE) ≤ VCADRC and >1.6 while C < 20% can be used as qualitative and quantitative evaluation criterion for forming better main skeleton structure of coarse aggregate. The new method of designing a DSAM based on meso parameter is intuitive and convenient, which considerably reduces the blindness and tediousness in the design of the asphalt mixture gradation. The engineering example also proves that the asphalt mixture has an excellent pavement performance and verifies the feasibility of the proposed design method.

An Improved Enhancement Algorithm Based on CNN Applicable for Weak Contrast Images

Image enhancement is commonly used in digital image processing applications. Through image enhancement, the features of the target object are enhanced to make it easier to identify. In order to realize image enhancement, a neural network with a trapezoidal convolution kernel is proposed in this paper. First, weak contrast images are generated using the images in SIDD, DND and RENOIR, which are public datasets used for image denoising. On this basis, pixel distribution characteristics of the R, G and B channels of the weak contrast images are analyzed, and the histogram distribution of the images is given, which can be used to determine the size of the network convolution kernels. Then, a multi-layer convolution neural network is constructed, which uses the original image as an input and outputs the noise map. This network includes convolution kernels of different sizes for the three channels of the original images, and the convolution kernel size is determined by calculating the mean square deviation of the pixels of the corresponding channel. The proposed algorithm provides visually pleasing contrast enhancement. In this paper, a public dataset for image enhancement and actual captured images are separately assessed, both the contrast experiment and ablation experiment results show that the algorithm proposed in this paper can achieve a higher Peak Signal to Noise Ratio (PSNR) and a higher Structural Similarity Index (SSIM) than other image enhancement algorithms.

Reconfigurable half-precision floating-point real/complex fused multiply and add unit

Multiplication followed by an addition/subtraction is the common operation in many digital signal and image processing applications. This paper presents a reconfigurable floating-point real/complex fused multiply and add (R/C-FMA) unit using small precision (IEEE-754-2008 16-bit half-precision) format. The developed FMA can be reconfigurable from real to complex based on the control bits. This architecture performs real FMA [(a × b) + c], complex FMA {[(a + ib) × (c + id)] + (e + if)} or mixed real and complex FMA {[(a + ib) × (c + id)] + e}. The field programmable gate array (FPGA) implementation of R/C-FMA design, utilises the modern features of inbuilt DSP blocks for mantissa multiplication and addition/subtraction. The efficient DSP usage for fp16 FMA design shows a 60% reduction in LUT area when compared to conventional fp32 FMA.

Reconfigurable half-precision floating-point real/complex fused multiply and add unit

Multiplication followed by an addition/subtraction is the common operation in many digital signal and image processing applications. This paper presents a reconfigurable floating-point real/complex fused multiply and add (R/C-FMA) unit using small precision (IEEE-754-2008 16-bit half-precision) format. The developed FMA can be reconfigurable from real to complex based on the control bits. This architecture performs real FMA [(a × b) + c], complex FMA {[(a + ib) × (c + id)] + (e + if)} or mixed real and complex FMA {[(a + ib) × (c + id)] + e}. The field programmable gate array (FPGA) implementation of R/C-FMA design, utilises the modern features of inbuilt DSP blocks for mantissa multiplication and addition/subtraction. The efficient DSP usage for fp16 FMA design shows a 60% reduction in LUT area when compared to conventional fp32 FMA.

Effects of Saururus chinensis BAILL Extract in Rats with Experimentally Chronic Constipation: An application of Clinical Pathology and Digital Image Processing

Effects of <i>Saururus chinensis BAILL</i> Extract in Rats with Experimentally Chronic Constipation: An application of Clinical Pathology and Digital Image Processing

Applications (Ideas) in Linear Algebra with Digital Image Processing. Can we Do, Teach, Motivate and Evaluate?

The application of new methodologies in higher education brings with it questions such as: “Can we do it?”, “Can we teach?”, “Can we motivate students?”, “Can we evaluate?”. During the last 8 years, in the Linear Algebra course of the Mechanical and Electromechanical Engineering of the Polytechnic of Coimbra-Institute of Engineering, it has been trying to answer these questions. The teaching methodology presented consists in the approach of Linear Algebra contents, using digital Image Processing applications. This new environment is intended to encourage students to learn the concepts and their manipulation in a productive and dynamic way. From the analysis of the studies and the student’s opinions (students’ adherence and motivation, questionnaires), it has been found that the use of this new teaching methodology helps the ‘learning to learn’ as long as it is well defined, with clear objectives for those who implements (teacher) and who interacts (students) with it.

Klasifikasi Kematangan Stroberi Berbasis Segmentasi Warna dengan Metode HSV

Classification of Strawberry Maturity Based on Color Segmentation using HSV Method. Manual fruit maturity classification has many limitations because it is influenced by human subjectivity. Hence, the application of digital image processing and artificial intelligence becomes more effective and efficient. This study aims to create a classification system that automatically divides strawberry maturity into three categories, namely not ripe, half-ripe, and ripe. The process of identifying the level of fruit maturity is based on the color characteristics Red, Green, Blue (RGB) value of the image. The method used for color segmentation is Hue, Saturation, Value (HSV) and for the classification of strawberry maturity using the Multi-Class Support Vector Machine (SVM) algorithm with a Radial Basic Function (RBF) kernel. Strawberry image data was retrieved using the Logitech C920 camera. The dataset consisted of 158 images of strawberries. The results showed that the classification of strawberry maturity using the multi-class SVM algorithm with kernel parameters RBF cost (C) = 10 and gamma (γ) = 10-3 produced the highest accuracy of 97%.

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Field Programmable Gate Array Applications—A Scientometric Review

Field Programmable Gate Array (FPGA) is a general purpose programmable logic device that can be configured by a customer after manufacturing to perform from a simple logic gate operations to complex systems on chip or even artificial intelligence systems. Scientific publications related to FPGA started in 1992 and, up to now, we found more than 70,000 documents in the two leading scientific databases (Scopus and Clarivative Web of Science). These publications show the vast range of applications based on FPGAs, from the new mechanism that enables the magnetic suspension system for the kilogram redefinition, to the Mars rovers’ navigation systems. This paper reviews the top FPGAs’ applications by a scientometric analysis in ScientoPy, covering publications related to FPGAs from 1992 to 2018. Here we found the top 150 applications that we divided into the following categories: digital control, communication interfaces, networking, computer security, cryptography techniques, machine learning, digital signal processing, image and video processing, big data, computer algorithms and other applications. Also, we present an evolution and trend analysis of the related applications.

Technology supporting health services for rural areas based on image processing

Limited health facilities, both medical equipment and health workers in developing countries and other rural areas in the world, bring significant problems for public health services. In order to make a correct diagnosis, it is necessary to support accurate data from health equipment that should be available at the health care centre. On the other hand, the development of technology, especially in the field of digital image processing is quite rapid. This paper discusses the application of digital image processing in solving problems concerning the limitations of health care facilities. In this paper, we will discuss the application of digital image processing in cataract screening, image quality improvement of ultrasonography, identification of leukocyte cells, and identification of ventricular areas and the hippocampus of magnetic resonance imaging images. The results show that the implementation of digital image processing techniques can help overcome some of the problems that arise due to the limitations of these health facilities.

Segmentasi Citra Medis untuk Deteksi Objek FAM pada Payudara Menggunakan Metode Sobel

Fibroa Adenoma Mammae (FAM) or called a benign tumor is the most common tumor found in the breast, often this disease is considered as breast cancer by some laymen, but this disease is different from cancer because Fibroadenoma (FAM) can grow in all parts of the breast, In identifying FAM, doctors or radiologists usually have to analyze carefully the images of Magnetic Resonance stored in the format of Digital Imaging Communication In Medicine (DICOM). This process is certainly quite time consuming. Thus the author feels the need to create a digital image processing application to help doctors or radiologists identify FAM in the breast by utilizing the segmentation process in medical images of USG results using the Sobel method. This method performs the original image segmentation process by detecting the edges of the image then the segmentation results are converted into binary images so the system can determine the FAM area. Medical image segmentation using the Sobel method is good for determining the edges of FAM objects because the edges can be clearly seen but for some image images with less resolution as previously tested, edge detection will be difficult to determine the edges of smooth objects and only form lines rough edges.

An Efficient Design of DCT Approximation Based on Quantum Dot Cellular Automata (QCA) Technology

Optimization for power is one of the most important design objectives in modern digital image processing applications. The DCT is considered to be one of the most essential techniques in image and video compression systems, and consequently a number of extensive works had been carried out by researchers on the power optimization. On the other hand, quantum-dot cellular automata (QCA) can present a novel opportunity for the design of highly parallel architectures and algorithms for improving the performance of image and video processing systems. Furthermore, it has considerable advantages in comparison with CMOS technology, such as extremely low power dissipation, high operating frequency, and a small size. Therefore, in this study, the authors propose a multiplier-less DCT architecture in QCA technology. The proposed design provides high circuit performance, very low power consumption, and very low dimension outperform to the existing conventional structures. The QCADesigner tool has been utilized for QCA circuit design and functional verification of all designs in this work. QCAPro, a very widespread power estimator tool, is applied to estimate the power dissipation of the proposed circuit. The suggested design has 53% improvement in terms of power over the conventional solution. The outcome of this work can clearly open up a new window of opportunity for low power image processing systems.

Digital Image Processing Application on Shallots Quality Determination

Shallot is a horticultural vegetable commodity that has high economic value. North Sulawesi is one of the central production of shallots which has several onion varieties developed and marketed. Technology that can help determine the quality of shallots is needed to simplify the marketing process. This study aims to simulate a system for determining the quality of shallots based on their color and size. The shallot bulb image of several different varieties is input for this system to be able to provide good and bad shallot marks.

On the design and implementation of a dual fisheye camera-based surveillance vision system

Image processing and computer vision have been a focus of researchers for decades in various application domains. This research is continuously rising with the rise of Artificial Intelligence in the fourth industrial revolution. One of the important digital image processing applications is to produce panorama images. The wide range of view a panorama image provides can be used in a variety range of applications which may include surveillance applications and remote robot operations. A panorama image is a combination of several individual natural looking images into a composite one to provide a wide field of view that may reach 360 degrees horizontally without any distortion. Wide-angle lenses provide a wide field of view, but using them alone does not necessarily make a panorama image. In this work the design and implementation of a wide-angle stereo vision system that suites many real-time applications is proposed. The system makes use of two wide-angle fisheye cameras where each camera covers around 170 degrees field of view. The horizontal angle between the cameras is 140 degrees. The cameras acquire the instantaneous overlapping images continuously and transmits them to a base station via a communication link. The base station calibrates, corrects, correlates and stitches the non-overlapping corrected images to a composite one. The resultant final image covers 310 degrees field of view. The system is of low computational complexity compared with previously implemented systems. It is tested on a laptop and on a standalone embedded computing device. The processing speed for the panorama image stitching including the correction of the fisheye barrel distortion on the laptop computer and the embedded computer is 11 fps, and 6 fps, respectively.