Image Processing Applications Research Articles

Attenuated color channel adaptive correction and bilateral weight fusion for underwater image enhancement

Due to the absorption and scattering of light and the influence of suspended particles, underwater images commonly exhibit color distortions, reduced contrast, and diminished details. This paper proposes an attenuated color channel adaptive correction and bilateral weight fusion approach called WLAB to address the aforementioned degradation issues. Specifically, a novel white balance method is first applied to balance the color channel of the input image. Moreover, a local-block-based fast non-local means method is proposed to obtain a denoised version of the color-corrected image. Then, an adaptive stretching method that considers the histogram's local features to get a contrast-enhanced version of the color-corrected image. Finally, a bilateral weight fusion method is proposed to fuse the above two image versions to obtain an output image with complementary advantages. Experimental studies are conducted on three benchmark underwater image datasets and compared with ten state-of-the-art methods. The results show that WLAB has a significant advantage over the comparative methods. Notably, WLAB exhibits a degree of independence from camera settings and enhances the precision of various image processing applications, including key points and saliency detection. Additionally, it demonstrates commendable adaptability in improving low-light and foggy images.

The Application of Tsallis Entropy Based Self-Adaptive Algorithm for Multi-Threshold Image Segmentation.

Tsallis entropy has been widely used in image thresholding because of its non-extensive properties. The non-extensive parameter q contained in this entropy plays an important role in various adaptive algorithms and has been successfully applied in bi-level image thresholding. In this paper, the relationships between parameter q and pixels' long-range correlations have been further studied within multi-threshold image segmentation. It is found that the pixels' correlations are remarkable and stable for images generated by a known physical principle, such as infrared images, medical CT images, and color satellite remote sensing images. The corresponding non-extensive parameter q can be evaluated by using the self-adaptive Tsallis entropy algorithm. The results of this algorithm are compared with those of the Shannon entropy algorithm and the original Tsallis entropy algorithm in terms of quantitative image quality evaluation metrics PSNR (Peak Signal-to-Noise Ratio) and SSIM (Structural Similarity). Furthermore, we observed that for image series with the same background, the q values determined by the adaptive algorithm are consistently kept in a narrow range. Therefore, similar or identical scenes during imaging would produce similar strength of long-range correlations, which provides potential applications for unsupervised image processing.

Efficient compressed storage and fast reconstruction of large binary images using chain codes

AbstractLarge binary images are used in many modern applications of image processing. For instance, they serve as inputs or target masks for training machine learning (ML) models in computer vision and image segmentation. Storing large binary images in limited memory and loading them repeatedly on demand, which is common in ML, calls for efficient image encoding and decoding mechanisms. In the paper, we propose an encoding scheme for efficient compressed storage of large binary images based on chain codes, and introduce a new single-pass algorithm for fast parallel reconstruction of raster images from the encoded representation. We use three large real-life binary masks to test the efficiency of the proposed method, which were derived from vector layers of single-class objects – a building cadaster, a woody vegetation landscape feature map, and a road network map. We show that the masks encoded by the proposed method require significantly less storage space than standard lossless compression formats. We further compared the proposed method for mask reconstruction from chain codes with a recent state-of-the-art algorithm, and achieved between $$12\%$$ 12 % and $$33\%$$ 33 % faster reconstruction on test data.

Polarization-selective terahertz absorber based on square cyclic graphene surface

Graphene-based absorbers are highly valued in terahertz technology due to their efficient performance. In this study, we theoretically propose a tunable, high sensitivity, single-band terahertz perfect absorber with polarization coherence that exhibits stable performance across a broad range of incidence angles. Simulation results show that the absorber's sensitivity to both TM and TE waves can be maintained at about 2 THz/RIU, with optimal values above 13.3 RIU−1 for TM waves and 12.9 RIU−1 for TE waves, and all Q-factors above 52. Furthermore, the impedance matching approach is utilized to explain the mechanism underlying its efficient absorption, and the electric field distribution is used to analyze the polarization coherence phenomena. We also propose a polarization-insensitive hetero-geometric structure based on the original structure, which allows for the separation of the absorption peaks by adjusting the geometrical parameters. These results show that the structure has a wide range of applications in aerospace, communication engineering, and image processing.

Affine phase retrieval of quaternion signals

Quaternion algebra H is an extension of the complex number field, which is a noncommutative associative algebra. In recent years, quaternionic Fourier analysis has interested some mathematicians due to its applications in signal analysis and image processing. This paper addresses quaternionic affine phase retrieval (QAPR) in quaternion Euclidean spaces H M , which aims to exactly recover a signal in H M from the magnitudes of its affine measurements. We introduce the concepts of QAPR and phaselift operator in H M . Then, we characterize QAPR in terms of the real Jacobian matrix, prove that 5M is the minimal measurement number for QAPR in H M , study the stability of QAPR-sequences, and use the phaselift techniques to give some sufficient conditions on QAPR for H M which provide us with a method to construct QAPR-sequences.

Higher order and optimized symmetric 2D FIR filter design and hardware architecture implementation using CSD-CSE

In this paper, an optimized and high-performance Two-Dimensional (2D) Finite Impulse Response (FIR) filter is designed and hardware architecture is implemented for image processing applications. The higher-order circular symmetric 2D FIR filter is designed using a modified McClellan Transformation called a P4 transformation. The perfect circular symmetry in the contour of the 2D FIR filter and less complexity and error are attained by this proposed P4 Transformation. Next, the designed filter coefficients are represented by the Canonical Signed Digit (CSD) number format to attain the multiplier-less design to avoid the power complexity multipliers. Further, to reduce the hardware complexity, the Common Subexpression Elimination (CSE) technique is utilized to reduce the number of adders and hence area and power are decreased. Each sub-filter corresponding to the CSD coefficient rows is realized and integrated as a 2D FIR filter using a Fully Direct-type architecture. This 2D FIR filter architecture is coded by Verilog and synthesized by Cadence tools in a 45 nm CMOS technology library. The Delay, Power Consumption (PC), and Area reports were generated by the Genus synthesis tool and compared with the state-of-the-art works. The PC, area, and delay of the proposed filter architecture are reduced to the minimum of 13.96%, 69.1%, and 1.4%, respectively when compared to the existing filter architectures. The maximum of 10.48 times and a minimum of 1.18 times of Area Delay Product (ADP), and a maximum of 10.69 times and a minimum of 1.063 times of Power Delay Product (PDP) are smaller than the existing 2D filter architectures.

Applications of Artificial Intelligence for Pediatric Cancer Imaging.

Artificial intelligence (AI) is transforming the medical imaging of adult patients. However, its utilization in pediatric oncology imaging remains constrained, in part due to the inherent scarcity of data associated with childhood cancers. Pediatric cancers are rare, and imaging technologies are evolving rapidly, leading to insufficient data of a particular type to effectively train these algorithms. The small market size of pediatric patients compared with adult patients could also contribute to this challenge, as market size is a driver of commercialization. This review provides an overview of the current state of AI applications for pediatric cancer imaging, including applications for medical image acquisition, processing, reconstruction, segmentation, diagnosis, staging, and treatment response monitoring. Although current developments are promising, impediments due to the diverse anatomies of growing children and nonstandardized imaging protocols have led to limited clinical translation thus far. Opportunities include leveraging reconstruction algorithms to achieve accelerated low-dose imaging and automating the generation of metric-based staging and treatment monitoring scores. Transfer learning of adult-based AI models to pediatric cancers, multiinstitutional data sharing, and ethical data privacy practices for pediatric patients with rare cancers will be keys to unlocking the full potential of AI for clinical translation and improving outcomes for these young patients.

Implementation and Efficient Analysis of Preprocessing Techniques in Deep Learning for Image Classification

Deep learning models have recently been preferred to perform certain image-processing tasks. Recently, with the increasing radiation, heat, and poor lighting conditions, the raw image samples may contain noisy and ambiguous information. To process these images, the deep learning model requires a large number of data samples to learn the missing information from other clear data samples. This necessitates training the neural network with a huge dataset. The researchers are now attempting to filter and improve such noisy images via preprocessing in order to provide valid and accurate feature information to the neural network layers. However, certain research studies claim that some useful information may be lost when the image is not preprocessed with an appropriate filter or enhancement technique. The MSA (meta-synthesis and analysis) approach is utilized in this work to present the impact of the image processing applications done with and without preprocessing steps. Also, this work summarizes the existing deep learning-based image processing models utilizing or not preprocessing steps in their implementation. This work has also found that 85% of the existing techniques involve a preprocessing step while developing a deep learning model. However, a maximum accuracy of 96.89% is observed on Sine-Net when it is implemented without a preprocessing and the same model gave 96.85% when implemented with preprocessing. This research provides various research insights on the requirement and non-requirement of preprocessing steps in a deep learning-based implementation.

Malignant thyroid lump multi classification by TIRADS using DBA with transfer learning

Thyroid diseases have developed into significant illnesses in recent decades. These diseases affect the thyroid glands and are caused by elevated thyroid hormone levels or infections in the thyroid organs. It is challenging to resolve thyroid diagnosis using conventional parametric and nonparametric statistical techniques since it can be viewed as a classification problem. However, there are certain barriers in the manner of obtaining both efficacy and accuracy in thyroid nodule diagnosis. Deep learning (DL) and machine learning (ML) models have emerged as useful instruments for the diagnosis of sickness in the modern era. For the purpose of diagnosing and classifying thyroid diseases, this research introduces a novel deep belief network (DBF) with transfer learning, known as DBNTL. In this study, the pre-processed image was first pre-processed using a conventional multiresolution bilateral technique, and then it was subjected to a novel segmentation technique called fusion pooling integrated U-net segmentation. The DBN with transfer learning model is used to classify and grade malignant thyroid nodules in compliance with thyroid imaging-reporting-and-data-system (TIRADS) guidelines. In this model, the model's weights are obtained by transfer learning. A major metric for evaluating the efficacy of biological image processing applications, good sensitivity and specificity (97.28 and 97.22, respectively) were obtained for the recommended modes.

Development of Power-Delay Product Optimized ASIC-Based Computational Unit for Medical Image Compression

The proliferation of battery-operated end-user electronic devices due to technological advancements, especially in medical image processing applications, demands low power consumption, high-speed operation, and efficient coding. The design of these devices is centered on the Application-Specific Integrated Circuits (ASIC), General Purpose Processors (GPP), and Field Programmable Gate Array (FPGA) frameworks. The need for low-power functional blocks arises from the growing demand for high-performance computational units that are part of high-speed processors operating at high clock frequencies. The operational speed of the processor is determined by the computational unit, which is the workhorse of high-speed processors. A novel approach to integrating Very Large-Scale Integration (VLSI) ASIC design and the concepts of low-power VLSI compatible with medical image compression was embraced in this research. The focus of this study was the design, development, and implementation of a Power Delay Product (PDP) optimized computational unit targeted for medical image compression using ASIC design flow. This stimulates the research community’s quest to develop an ideal architecture, emphasizing on minimizing power consumption and enhancing device performance for medical image processing applications. The study uses area, delay, power, PDP, and Peak Signal-to-Noise Ratio (PSNR) as performance metrics. The research work takes inspiration from this and aims to enhance the efficiency of the computational unit through minor design modifications that significantly impact performance. This research proposes to explore the trade-off of high-performance adder and multiplier designs to design an ASIC-based computational unit using low-power techniques to enhance the efficiency in power and delay. The computational unit utilized for the digital image compression process was synthesized and implemented using gpdk 45 nm standard libraries with the Genus tool of Cadence. A reduced PDP of 46.87% was observed when the image compression was performed on a medical image, along with an improved PSNR of 5.89% for the reconstructed image.

Note on Intuitionistic Fuzzy Metric-like Spaces with Application in Image Processing

Recently, the fixed-point theorem for fuzzy contractive mappings has been investigated within the framework of intuitionistic fuzzy metric-like spaces. This interesting topic was explored through the utilization of G-Cauchy sequences as defined by Grabiec. The aim of this study is to enhance the aforementioned results in a few aspects. Initially, the proof of the fixed-point theorem is simplified and condensed, allowing for potential generalization to papers focusing on similar fixed-point analyses. Furthermore, instead of G-Cauchy sequences, the classical Cauchy sequences proposed by George and Veeramani are examined, incorporating an additional condition on the fuzzy metric. Within this context, a solution to an old unresolved question posed by Gregory and Sapena is provided. The findings are reinforced by relevant examples. Finally, the introduced fuzzy metrics are applied to the field of image processing.

Analyzing performance: Error-efficient, low-power recursive inexact multipliers for CNN applications

In present scenario, significant data manipulations can be espied in image processing and Convolutional Neural Networks (CNNs) applications. The quality performance of these applications relies ponderously on arithmetic operations. In this context, Approximate Computing frequently finds its application, where a certain margin of error is deemed acceptable, on prioritizing circuit speed over absolute precision and the common focus lies on optimizing the binary multiplier which is a crucial yet power-intensive component, so as to streamline the complexity of above mentioned applications. Furthermore, Inexact Multipliers (IMs) are developed by using recursive method, thereby purveying enhanced design and error metrics. This paper introduces higher order Recursive-Based Inexact Multipliers (RBIMs), which integrates the suggested 4-bit IMs, and exact multipliers along with the recursive method, aiming to provide a delicate balance between design performance metrics and error tolerance. For evaluating the proposed approach, most of the existing and recently suggested IMs operating at 8-bit and 16-bit, are designed in Verilog, coded in Python, synthesized and simulated using Cadence Register Transfer Language (RTL) Compiler, Google Colabs. Eventually, the simulation results bespeak a notable improvement in power consumption, with the 8-bit and 16-bit RBIMs by outperforming the previous designs up to 36.9 %, and 35.5 % respectively. Further, the efficacies of the RBIMs are validated through its usage in image processing and CNN applications.

Quarter-wave Pancharatnam-Berry phase gradient liquid crystal-enabled dual-polarization optical edge detection.

Here, we present a novel, to the best of our knowledge, optical edge detection scheme that can be operated in both linear and circular polarization modes, leveraging an optical spatial differentiator constructed by quarter-wave Pancharatnam-Berry (P-B) phase gradient element. After explaining the theoretical mechanism, we utilize a quarter-wave P-B phase liquid crystal polarization grating to validate the dual-polarization optical edge detection capability. We demonstrate that the orientation of linear polarization and the spin of circular polarization dictate the transition between edge and bright-field images. Besides, the linear and circular polarization modes exhibit broadband and monochromatic responsive properties, respectively. This mechanism, dependent on wavelength and polarization, holds promise for applications in color image processing, chiral sensing imaging, and polarization-entangled quantum imaging.

A quantitative study of the microstructure of Indian Gondwana shale: a fractal and algebraic topology approach

This paper covers a novel micro-level application of image processing in understanding the topological and petrophysical properties of Indian Gondwana shale using X-ray computed microtomography images. The complexity and randomness in the pore system are explained through the concept of fractal dimension (FD). In this paper, a quantitative analysis of 2D and 3D fractal dimensions of pores, grains and interfaces was performed for Indian Gondwana shale, using the box-counting method. A pore network is formed by the connection of many subpore clusters, each with a different volume. Hence, an image segmentation algorithm was applied to label different subclusters, and subsequently an analysis of FD was carried out on such subclusters of pores and grains. We implemented a novel application of Betti numbers (B0, B1 and B2) and Euler characteristics on our sample and calculated the possible flow channels of the sample. The FD of grains was found to be greater than the FD of the pore–grain interfaces, while the FD of pores was found to have the smallest value. Consequently, we also observed how the FD of both pores and grains was majorly controlled by the largest subcluster, and during fluid intrusion we observed a significant decrease in the FD of pores. Finally, the pore network with a larger B0 and larger difference of B1 was proved to be best for the storage of hydrocarbons and for fluid movement along more flow channels.

Specific Investigations Concerning the Improveable Multiplier Architecture of High-Speed and Area-Efficient Adders

Over the past few decades, the growth of portable devices such as laptops, mobile phone and personal digital assistant has resulted in increasing demand for complex functionality with effective computation. The present day technology is known for digital systems with very high computing capabilities. The demand for high speed, low power integrated circuits for portable devices has become crucial. The never ending growing complexities of integrated circuit for future devices pose a challenging task for integrated circuit designer. Cost effective integrated circuits requires the design meeting out the challenging task to optimize power, area with high performance. Hence this research focuses on the optimization of area, power and speed of Arithmetic circuits, Very Large Scale Integrated circuits (VLSI) are widely used in Arithmetic circuits, Digital Signal Processing (DSP), Image and Video Processing applications

Image Multiplication Using Novel 4:1 Approximate Compressor*

Energy-efficient designs are the need of the hour for signal and image processing applications that are error-tolerant. The paper proposes a new 4:1 approximate arithmetic-based compressor developed by analyzing the probability-based tactics that have a higher degree of precision when implemented. Approximate n × n multiplier is designed using the proposed varying probability-based compressors. To validate the effectiveness of the new compressor, a 16 × 16 multiplier is designed. The n × n bit multiplier will have 2 n columns of partial products(PP), in which the least significant n−1 columns are designed using the novel approximate 4:1 compressor and most n + 1 columns are designed with the exact compressor. The results of the 16-bit multiplier are superior to the performance of the exact multiplier in terms of power, area, power-delay-product (PDP), and area-delay-product (ADP) typically by 39 % , 20 % , 44 % , and 26 % respectively for Design 1 and 14 % , 24 % , 30 % , and 21 % , respectively for Design 2. The Peak Signal to Noise Ratio(PSNR), Mean Square Error(MSE), and Structural Similarity Index Measure(SSIM) for the output images processed by new approximate multipliers are acceptable when compared with precision and energy.

Rapid inverse design of metasurfaces with an asymmetric transfer function for all-optical image processing using a mode matching model.

Metasurfaces have recently emerged as an ultra-compact solution to perform all-optical image processing, including phase contrast imaging. Most metasurfaces used in imaging processing applications operate over a restricted numerical aperture. This limitation imposes constraints on the discernible features that can be effectively visualized and consequently leads to the appearance of undesirable artifacts. Engineering a metasurface that exhibits an asymmetric linear optical transfer function over a relatively large numerical aperture, while maintaining a strong contrast, has proven to be a challenge. In this study, we present a novel approach to designing relatively high numerical aperture and contrast nonlocal metasurfaces (up to a numerical aperture of around 0.5 and an intensity contrast of approximately 50%) with unit cells consisting of several plasmonic nanorods through the use of a rapid, quasi-analytic mode-matching technique, coupled with an optimization algorithm. The combination of these methods facilitates the rapid conceptualization of nonintuitive arrangements of metallic nanoparticles, specifically tailored to perform phase contrast imaging. These designs hold substantial promise in the development of ultra-compact imaging systems.

Application of machine learning methods and medical image processing in solving the problem of detecting stenoses of the middle cerebral artery according to computed tomographic angiography data

Application of machine learning methods and medical image processing in solving the problem of detecting stenoses of the middle cerebral artery according to computed tomographic angiography data

On a Local and Nonlocal Second-Order Boundary Value Problem with In-Homogeneous Cauchy–Neumann Boundary Conditions—Applications in Engineering and Industry

A qualitative study for a second-order boundary value problem with local or nonlocal diffusion and a cubic nonlinear reaction term, endowed with in-homogeneous Cauchy–Neumann (Robin) boundary conditions, is addressed in the present paper. Provided that the initial data meet appropriate regularity conditions, the existence of solutions to the nonlocal problem is given at the beginning in a function space suitably chosen. Next, under certain assumptions on the known data, we prove the well posedness (the existence, a priori estimates, regularity, uniqueness) of the classical solution to the local problem. At the end, we present a particularization of the local and nonlocal problems, with applications for image processing (reconstruction, segmentation, etc.). Some conclusions are given, as well as new directions to extend the results and methods presented in this paper.

A Fisher-type evolution equation with applications in image processing

The aim of this paper is to present some applications of the Neumann Laplacian in image processing, along with the necessary mathematical background. We prove weak and strong versions of the maximum principle for weak solutions of elliptic and parabolic problems and apply them to a Fisher K.P.P.-type equation. The original contribution lies in the application of this equation in image processing, where various diffusion-like effects can be achieved. Additionally, a review of the basics of linear and nonlinear PDEs with Neumann boundary conditions is provided, along with updated bibliography and recent qualitative results. There are also some new theoretical results developed in this work.