Modern Image Processing Research Articles

A nonparametric measure of noise in x-ray diagnostic images—mammography

Objective. In x-ray diagnostics, modern image reconstruction or image processing methods may render established methods of image quality assessment inadequate. Task specific quality assessment by using model observers has the disadvantage of being very labour-intensive. Therefore, it appears highly desirable to develop novel image quality parameters that neither rely on the linearity and the shift-invariace of the imaging system nor require the acquisition of hundreds of images as is necessary for the application of model observers, and which can be derived directly from diagnostic images. Approach. A new measure for the noise based on non-maximum-suppression images is defined and its properties are explored using simulated images before it is applied to an exposure series of mammograms of a homogeneous phantom and a 3D-printed breast phantom to demonstrate its usefulness under realistic conditions. Main results. The new noise parameter cannot only be derived from images with a homogeneous background but it can be extracted directly from images containing anatomic structures and is proportional to the standard deviation of the noise. At present, the applicability is restricted to mammography, which satisfies the assumption of short covariance length of the noise. Significance. The new measure of the noise is but a first step of the development of a set of parameters that are required to quantify image quality directly from diagnostic images without relying on the assumption of a linear, shift-invariant system, e.g. by providing measures of sharpness, contrast and structural complexity, in addition to the noise measure. For mammography, a convenient method is now available to quantify noise in processed diagnostic images.

Development of digital image processing algorithms based on the Winograd method in general form and analysis of their computational complexity

The fast increase of the amount of quantitative and qualitative characteristics of digital visual data calls for the improvement of the performance of modern image processing devices. This article proposes new algorithms for 2D digital image processing based on the Winograd method in a general form. An analysis of the obtained results showed that the use of the Winograd method reduces the computational complexity of image processing by up to 84% compared to the traditional direct digital filtering method depending on the filter parameters and image fragments, while not affecting the quality of image processing. The resulting Winograd method transformation matrices and the algorithms developed can be used in image processing systems to improve the performance of the modern microelectronic devices that carry out image denoising, compression, and pattern recognition. Research directions that show promise for further research include hardware implementation on a field-programmable gate array and application-specific integrated circuit, development of algorithms for digital image processing based on the Winograd method in a general form for a 1D wavelet filter bank and for stride convolution used in convolutional neural networks.

Normalized Weighting Schemes for Image Interpolation Algorithms

Image interpolation algorithms pervade many modern image processing and analysis applications. However, when their weighting schemes inefficiently generate very unrealistic estimates, they may negatively affect the performance of the end-user applications. Therefore, in this work, the author introduced four weighting schemes based on some geometric shapes for digital image interpolation operations. Moreover, the quantity used to express the extent of each shape’s weight was the normalized area, especially when the sums of areas exceeded a unit square size. The introduced four weighting schemes are based on the minimum side-based diameter (MD) of a regular tetragon, hypotenuse-based radius (HR), the virtual pixel length-based height for the area of the triangle (AT), and the virtual pixel length for hypotenuse-based radius for the area of the circle (AC). At the smaller scaling ratio, the image interpolation algorithm based on the HR scheme scored the highest at 66.6% among non-traditional image interpolation algorithms presented. However, at the higher scaling ratio, the AC scheme-based image interpolation algorithm scored the highest at 66.6% among non-traditional algorithms presented, and, here, its image interpolation quality was generally superior or comparable to the quality of images interpolated by both non-traditional and traditional algorithms.

4D: A Real-Time Driver Drowsiness Detector Using Deep Learning

There are a variety of potential uses for the classification of eye conditions, including tiredness detection, psychological condition evaluation, etc. Because of its significance, many studies utilizing typical neural network algorithms have already been published in the literature, with good results. Convolutional neural networks (CNNs) are employed in real-time applications to achieve two goals: high accuracy and speed. However, identifying drowsiness at an early stage significantly improves the chances of being saved from accidents. Drowsiness detection can be automated by using the potential of artificial intelligence (AI), which allows us to assess more cases in less time and with a lower cost. With the help of modern deep learning (DL) and digital image processing (DIP) techniques, in this paper, we suggest a CNN model for eye state categorization, and we tested it on three CNN models (VGG16, VGG19, and 4D). A novel CNN model named the 4D model was designed to detect drowsiness based on eye state. The MRL Eye dataset was used to train the model. When trained with training samples from the same dataset, the 4D model performed very well (around 97.53% accuracy for predicting the eye state in the test dataset). The 4D model outperformed the performance of two other pretrained models (VGG16, VGG19). This paper explains how to create a complete drowsiness detection system that predicts the state of a driver’s eyes to further determine the driver’s drowsy state and alerts the driver before any severe threats to road safety.

DEVELOPMENT A HARDWARE AND SOFTWARE SYSTEM TO DETERMINE THE COORDINATES OF AUTONOMOUS VEHICLES BASED ON IMAGE PROCESSING

Modern image processing techniques play an important role in solving this problem. One of the elements for control and navigation of unmanned aerial vehicles is trajectory estimation. This is seen as an important task for identifying the location of the device and objects in the environment depending on the distance between them, as well as with the help of modern consumer devices that allow image recording. Systems that provide navigation accompaniment are consists of hardware and software. The complexity of the hardware varies from very simple options. Typically, software is developed and used for a specific hardware platform. This article contains a description of the programming environment in which the modeling and evaluation of the effectiveness of algorithms were carried out, which proposes to improve the accuracy of the control system, based on image processing of the surrounding area.

Biometric technology for personal identification and authentication: fingerprint

This paper presents an efficient fingerprint verification algorithm that improves matching accuracy. Algorithms for image processing for biometric technologies of identification and authentication of a person using fingerprints are considered. They are essential tools for improving performance across a wide range of industries. The recognition system is designed for storing data, further processing it, identifying and displaying fingerprints. In forensics, modern image processing algorithms are used and provide a significant improvement in work results. Fingerprint images are degraded and damaged due to changes in skin and print conditions. Thus, image enhancement techniques are used before feature points are detected and fine details are extracted. Templates are generated based on feature vector extraction and databases are created for verification and identification of fingerprint images. The method of experimental research is carried out, the process of processing the identification results is considered. To solve this problem, a recognition system was proposed, which is designed to store data, further process it, identify and display fingerprints. They are used in places of high secrecy as a kind of password access key based on scanning and verifying fingerprints with the FVC2002 database. The results obtained show that the proposed algorithm is more efficient and reliable.

The possibility of using close-range photogrammetry in the inventory of historic complex basements - case study

In the community, historical objects play the role of witness to past history. Due to that fact, it is necessary to preserve and reconstruct cultural heritage objects and sites for the future generation. Image-based photogrammetric methods have been widely applied for this purpose for many years. Nowadays, Terrestrial Laser Scanning (TLS; range-based method), due to its advantages such as speed of data acquisition, high accuracy and independence from light conditions, is increasingly used in the inventory of complex historic buildings. Despite this, the development of modern image processing methods, i.e. Structure-from-Motion (SfM) and Multi-View Stereo (MVS), has meant that close-range photogrammetric techniques are still competitive with TLS. The article aimed to present the possibility of using close-range photogrammetry to inventory historic complex basements. Laser scanning was performed as part of the measurements (with a Z+F 5006h scanner), and a series of close-range images were taken with a full-frame non-metric Canon 5D Mark II camera. Based on the combined SfM and MVS methods, a dense point cloud was generated, which in a subsequent data processing step served as the basis for generating 3D models and cross-sections. To assess the quality of the generated documentation, the TLS data were used as ground-truth data, and the shape and cross-section mapping quality was compared. It is evident from the investment presented that the use of close-range photogrammetry methods makes it possible to generate documentation that meets the requirements of architectural studies and similar shape accuracy for historic complex basements.

Histogram equalization using a selective filter

AbstractMany popular modern image processing software packages implement a naïve form of histogram equalization. This implementation is known to produce histograms that are not truly uniform. While exact histogram equalization techniques exist, these may produce undesirable artifacts in some scenarios. In this paper we consider the link between the established continuous theory for global histogram equalization and its discrete implementation, and we formulate a novel histogram equalization technique that builds upon and considerably improves the naïve approach. We show that we can linearly interpolate the cumulative distribution of a low-bit image by approximately dequantizing its intensities using a selective box filter. This helps to distribute the intensities more evenly. The proposed algorithm is subsequently evaluated and compared with existing works in the literature. We find that the method is capable of producing an equalized histogram that has a high entropy, while distances between similar intensities are preserved. The described approach has implications on several related image processing problems, e.g., edge detection.

Research on Multichannel 3D Oil Painting Style Rendering Model Based on 3DS Max

People are becoming less comfortable in content as a form of data throughout this period of growing scientific and technological advancements. Oil painting also was challenged in unprecedented ways by modern image processing techniques as person’s primary source of data, and it had a tremendous effect on the domain of artistic creativity. Therefore, we present an upgraded convolutional neural network (U-CNN) for the enhancement of style rendering model addressing the aforementioned challenges. Initially, the datasets are gathered that are denoised and enhanced in the preprocessing stage to eliminate noise and improve the quality of the data. Gabor filter bank (GFB) is employed in the feature extraction stage to extract several features from the normalized data. For the application of style rendering model, the proposed approach is utilized. Moreover, through applying the 3DS MAX model, the three-dimensional (3-D) oil painting is generated. Finally, the performance of the proposed approach is examined and compared with other existing approaches to obtain the proposed approach with the greatest effectiveness. The findings are depicted in chart form by employing the origin tool.

Estimating the volume of the 1978 Rissa quick clay landslide in Central Norway using historical aerial imagery

Abstract Quick clay is found across Scandinavia and is especially prominent in south-eastern and central Norway. Quick clay is prone to failure and can cause landslides with high velocities and large run-outs. The 1978 Rissa landslide is one of the best-known quick clay landslides to have occurred in the last century, both due to its size and the fact that it was captured on film. In this article, we utilise Structure from Motion Multi-View Stereo (SfM-MVS) photogrammetry to process historical aerial photography from 1964 to 1978 and derive the first geodetic volume of the Rissa landslide. We found that the landslide covered a total onshore area of 0.36 km2 and had a geodetic volume of 2.53 ± 0.52 × 106 m3 with up to 20 m of surface elevation changes. Our estimate differs profusely from previous estimates by 43–56% which can partly be accounted for our analysis not being able to measure the portion of the landslide that occurred underwater, nor account for the material deposited within the landslide area. Given the accuracy and precision of our analyses, we believe that the total volume of the Rissa landslide may have been less than originally reported. The use of modern image processing techniques such as SfM-MVS for processing historical aerial photography is recommended for understanding landscape changes related to landslides, volcanoes, glaciers, or river erosion over large spatial and temporal scales.

Quantification of cracks in concrete thin sections considering current methods of image analysis.

Image analysis is used in this work to quantify cracks in concrete thin sections via modern image processing. Thin sections were impregnated with a yellow epoxy resin, to increase the contrast between voids and other phases of the concrete. By the means of different steps of pre-processing, machine learning and python scripts, cracks can be quantified in an area of up to 40cm2 . As a result, the crack area, lengths and widths were estimated automatically within a single workflow. Crack patterns caused by freeze-thaw damages were investigated. To compare the inner degradation of the investigated thin sections, the crack density was used. Cracks in the thin sections were measured manually in two different ways for validation of the automatic determined results. On the one hand, the presented work shows that the width of cracks can be determined pixelwise, thus providing the plot of a width distribution. On the other hand, the automatically measured crack length differs in comparison to the manually measured ones.

BIT OPERATIONS WITH ELEMENTS OF THE RSA ALGORITHM IN ENCRYPTION-DECRYPTION OF COLOR IMAGES

An image as a stochastic signal is one of the most common forms of information. Protecting images from unauthorized access and applying is a correspondingly urgent task. This causes the use of well-known classical encryption methods in the case of image encryption. But the image is a signal that possesses, in addition to typical informativeness, also visual informativeness. Informativeness for modern image processing methods makes it possible to ensure unauthorized access. Creating an attack on an encrypted image is possible in two ways: by traditional hacking of encryption methods, or by classical methods of visual image processing (filtering, highlighting contours, etc.). In this regard, one more requirement is put forward to encryption methods in the case of their application concerning images - this is the complete noise of the encrypted image. This is necessary so that the use of visual image processing methods becomes impossible. The RSA algorithm is one of the most widely known industrial standards for encrypting signals. Unlike symmetric encryption, in an open-key encryption scheme, it is impossible to calculate the decryption procedure, knowing the encryption procedure. Namely, the working time of the algorithm for calculating the decryption procedure is so great that it cannot be implemented on any modern computers, as well as on computers of the future. Such coding schemes are called asymmetric. Therefore, the urgent task is to implement the application of the RSA algorithm so that when encrypting an image: – the cryptographic stability of the RSA algorithm has not become worse; – the full image noise was achieved to prevent the use of visual image processing techniques. The algorithm of elements of the RSA algorithm, as the most resistant to unauthorized decryption of signals, and bitwise operations for a compatible combination during encryption and decryption of images is proposed by the authors. Encryption - decryption is performed without additional noise. The proposed algorithm is applied to images in which there are strictly extracted contours. Elements of the RSA algorithm are assigned to perform bitwise operations on the intensity values of pixels of a color image. The developed algorithm has higher cryptographic stability compared to the traditional RSA algorithm. The authors described the possibilities of using elements of the RSA algorithm in bitwise transformations when encrypting and decrypting images. The results of encryption simulation for cryptographic transformations of color images of a given dimension are presented. Modified models and algorithmic procedures of key formation processes of direct and inverse cryptographic transformations have been developed. They are reduced to elemental mathematical operations.

Вопросы выбора размера пикселя при топографической аэрофотосъемке

The existing regulatory documents on photogrammetric works are technologically outdated. They neither take into account the peculiarities of aerial photography with digital cameras, the navigation equipment used and modern image processing methods, nor regulate the technique of calculating the pixel size on the ground. In order to select the pixel size in the terrain for aerial photography with topographic requirements concerning to the results, the method of multivariate analysis of the input data is proposed. It is supposed to ensure the minimum pixel size on the ground according to such criteria as the accuracy of the aerial triangulation results, the accuracy of building a digital elevation model for orthotransformation, the possibility of the objects interpretation with a specified minimum size and consideration of camera exposure parameters. To determine the accuracy criteria, we used formulas for pre- calculation of spatial phototriangulation accuracy with multiple choice of parameters. Examples of pixel size selection in the terrain at designing aerial photography by an amateur camera for the purposes of large-scale mapping are considered. Conclusions on the necessity of solving the issues of selecting parameters of large scale aerial photography, taking into account multiple input data and used aerial survey equipment are made.

Analyzing fibrous tissue pattern in fibrous dysplasia bone images using deep R-CNN networks for segmentation

Predictive health monitoring systems help to detect human health threats in the early stage. Evolving deep learning techniques in medical image analysis results in efficient feedback in quick time. Fibrous dysplasia (FD) is a genetic disorder, triggered by the mutation in Guanine Nucleotide binding protein with alpha stimulatory activities in the human bone genesis. It slowly occupies the bone marrow and converts the bone cell into fibrous tissues. It weakens the bone structure and leads to permanent disability. This paper proposes the study of FD bone image analyzing techniques with deep networks. Also, the linear regression model is annotated for predicting the bone abnormality levels with observed coefficients. Modern image processing begins with various image filters. It describes the edges, shades, texture values of the receptive field. Different types of segmentation and edge detection mechanisms are applied to locate the tumor, lesion, and fibrous tissues in the bone image. Extract the fibrous region in the bone image using the region-based convolutional neural network algorithm. The segmented results are compared with their accuracy metrics. The segmentation loss is reduced by each iteration. The overall loss is 0.24% and the accuracy is 99%, segmenting the masked region produces 98% of accuracy, and building the bounding boxes is 99% of accuracy.

Optical properties and surface dynamics analyses of homojunction and hetrojunction Q/ITO/ZnO/NZO and Q/ITO/ZnO/NiO thin films

The aim of the present study is to verify how alternations of annealing temperatures modify optical and micromorphological properties of n-ZnO/p-NZO and n-ZnO/p-NiO multilayers. As can be seen, the optical analysis of homojunctions and hetrojunction thin films shows a red shift by increasing annealing temperature which means that annealing can shift absorption edge coefficient to the lower values of energies. Also, the effective parameters on optical properties of homo- and hetrojunction thin films such as optical band gap, Urbach energy, steepness parameter, and skin depth have been studied by their transmittance spectra. Moreover, the micromorphology of n/p ZnO homo- and heterojunctions transparent diodes have been studied by atomic force microscopy in combination with modern image processing techniques. The nano scaled stereometric analysis provides significant insights into the influence of preparation process on surface texture geometry. According to the results extracted from AFM micrographs by MountainsMap® software, it is observed that Q/ITO/ZnO/NZO sample with 300 °C annealing temperature has the normal distribution of peaks with the highest percentage of isotropy. In addition, it is observed that Q/ITO/ZnO/NiO with 500 °C annealing temperature has the most regular topography.

No-reference stereoscopic image quality assessment using quaternion wavelet transform and heterogeneous ensemble learning

As the demand for high-quality stereo images has grown in recent years, stereoscopic image quality assessment (SIQA) has become an important research area in modern image processing technology.In this paper, we propose a no-reference stereoscopic image quality assessment (NR-SIQA) model using heterogeneous ensemble learning ‘quality-aware’ features from luminance image, chrominance image, disparity and cyclopean images via quaternion wavelet transform (QWT). Firstly, luminance image and chrominance image are generated by CIELAB color space as monocular perception, and the novel disparity and cyclopean images are utilized to complement with monocular information. Then, a number of ‘quality-aware’ features in the quaternion wavelet domain are discovered, including entropy, texture features, energy features, energy differences features and MSCN coefficients of high frequency sub-band. Finally, a heterogeneous ensemble model via support vector regression (SVR) & extreme learning machine (ELM) & random forest (RF) is proposed to predict quality score, and bootstrap sampling and rotated feature space are used to increase the diversity of data distribution. Comparing with the state-of-the-art NR-SIQA models, experimental results on four public databases prove the accuracy and robustness of the proposed model.

Restoration and quality improvement of distorted tribal artworks using Particle Swarm Optimization (PSO) technique along with nonlinear filtering

Cultural artworks that are not properly cared for, degrades over time. However modern image processing techniques proves to be a boon for such artworks. Various techniques have been developed time to time for different aspects of image processing like noise removal, improving brightness and image quality, etc. One of such techniques is PSO (Particle Swarm Optimization) that can be used for image restoration. In this paper a combination of median filter and PSO has been used for the quality improvement of the distorted tribal artworks and the results are compared with few HE (Histogram Equalization) techniques like the CLAHE, BBHE and DSIHE. The comparison is done on the basis of various image quality assessment parameters, namely PSNR, AMBE, NMSE, CPP and MSE.

Investigation on quality enhancement of old and fragile artworks using non-linear filter and histogram equalization techniques

Traditional Cultural artwork requires utmost care and may tend to degrade if not preserved properly. These old artworks can regain their quality with the help of modern image processing tools. Image processing forms an integral part of the scientific and research community. Removal of the unwanted noise forms the first step in image processing. Various filters have been developed and modified for noise removal over the years. Enhancement of the image by improving its contrast or brightness is also a crucial and important step. This paper discusses about the improvement of image quality by implementing a combination of the non-linear median filter with three different histogram equalization techniques (CLAHE, BBHE, and DSIHE). MATLAB is used for the implementation of the above mentioned combinations. Reference quality metrics parameters like AMBE, PSNR, NMSE as well as Non-reference quality metrics parameters like CPP, BRISQUE, PIQE are determined for the quality assessment of the processed image.

Quantitative Assessment of Experimental Ocular Inflammatory Disease.

Ocular inflammation imposes a high medical burden on patients and substantial costs on the health-care systems that mange these often chronic and debilitating diseases. Many clinical phenotypes are recognized and classifying the severity of inflammation in an eye with uveitis is an ongoing challenge. With the widespread application of optical coherence tomography in the clinic has come the impetus for more robust methods to compare disease between different patients and different treatment centers. Models can recapitulate many of the features seen in the clinic, but until recently the quality of imaging available has lagged that applied in humans. In the model experimental autoimmune uveitis (EAU), we highlight three linked clinical states that produce retinal vulnerability to inflammation, all different from healthy tissue, but distinct from each other. Deploying longitudinal, multimodal imaging approaches can be coupled to analysis in the tissue of changes in architecture, cell content and function. This can enrich our understanding of pathology, increase the sensitivity with which the impacts of therapeutic interventions are assessed and address questions of tissue regeneration and repair. Modern image processing, including the application of artificial intelligence, in the context of such models of disease can lay a foundation for new approaches to monitoring tissue health.

Real-time implementation of fast discriminative scale space tracking algorithm

Real-time object tracking is an important step of many modern image processing applications. The efficient hardware design of real-time object tracker must achieve the desired accuracy while satisfying the frame rate requirements for a variety of image sizes. The existing methods of visual tracking employ sophisticated algorithms and challenge the capabilities of most embedded architectures. Discriminative scale space tracking is one algorithm that is capable of demonstrating good performance with affordable complexity. It has a high degree of parallelism which can be exploited for efficient implementation of reconfigurable hardware architectures. This paper proposes a real-time implementation of the discriminative scale-space tracker on FPGA for the major blocks. A careful design exploration of core mathematical operations of the tracking algorithm is performed to improve their hardware utilization and timing performance. Among the core functional units optimized in this work, the discrete Fourier transform achieves a computational time improvement of 92% relative to existing works, QR factorization achieves a 2.3times reduction in resource utilization, and singular value decomposition yields a 3.8times improvement in processing time. The proposed data path architecture is designed using Vivado HLS tool set and implemented for Zync Zed Board (xc7z020clg484-1). For an input image size of 320 times 240, the proposed architecture achieves a mean 25.38 fps.