Image Resolution Enhancement Research Articles

Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration

Background and objectiveFor the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate camera calibration is imperative. However, when the checkerboard images for calibration are captured using a surgical microscope having high magnification, blur owing to the narrow depth of focus and blocking artifacts caused by limited resolution around the fine edges occur. These artifacts strongly affect the localization of corner points of the checkerboard in these images, resulting in inaccurate calibration, which leads to a large displacement in augmented reality. To solve this problem, in this study, we proposed a novel target-specific deep learning network that simultaneously enhances both the blur and spatial resolution of an image for surgical zoom lens camera calibration. MethodsAs a scheme of an end-to-end convolutional deep neural network, the proposed network is specifically intended for the checkerboard image enhancement used in camera calibration. Through the symmetric architecture of the network, which consists of encoding and decoding layers, the distinctive spatial features of the encoding layers are transferred and merged with the output of the decoding layers. Additionally, by integrating a multi-frame framework including subpixel motion estimation and ideal reference image with the symmetric architecture, joint image deblurring and enhanced resolution were efficiently achieved. ResultsFrom experimental comparisons, we verified the capability of the proposed method to improve the subjective and objective performances of surgical microscope calibration. Furthermore, we confirmed that the augmented reality overlap ratio, which quantitatively indicates augmented reality accuracy, from calibration with the enhanced image of the proposed method is higher than that of the previous methods. ConclusionsThese findings suggest that the proposed network provides sharp high-resolution images from blurry low-resolution inputs. Furthermore, we demonstrate superior performance in camera calibration by using surgical microscopic images, thus showing its potential applications in the field of practical surgical navigation.

A deep learning approach for deriving wheat phenology from near-surface RGB image series using spatiotemporal fusion

Accurate monitoring of wheat phenological stages is essential for effective crop management and informed agricultural decision-making. Traditional methods often rely on labour-intensive field surveys, which are prone to subjective bias and limited temporal resolution. To address these challenges, this study explores the potential of near-surface cameras combined with an advanced deep-learning approach to derive wheat phenological stages from high-quality, real-time RGB image series. Three deep learning models based on three different spatiotemporal feature fusion methods, namely sequential fusion, synchronous fusion, and parallel fusion, were constructed and evaluated for deriving wheat phenological stages with these near-surface RGB image series. Moreover, the impact of different image resolutions, capture perspectives, and model training strategies on the performance of deep learning models was also investigated. The results indicate that the model using the sequential fusion method is optimal, with an overall accuracy (OA) of 0.935, a mean absolute error (MAE) of 0.069, F1-score (F1) of 0.936, and kappa coefficients (Kappa) of 0.924 in wheat phenological stages. Besides, the enhanced image resolution of 512 × 512 pixels and a suitable image capture perspective, specifically a sensor viewing angle of 40° to 60° vertically, introduce more effective features for phenological stage detection, thereby enhancing the model’s accuracy. Furthermore, concerning the model training, applying a two-step fine-tuning strategy will also enhance the model’s robustness to random variations in perspective. This research introduces an innovative approach for real-time phenological stage detection and provides a solid foundation for precision agriculture. By accurately deriving critical phenological stages, the methodology developed in this study supports the optimization of crop management practices, which may result in improved resource efficiency and sustainability across diverse agricultural settings. The implications of this work extend beyond wheat, offering a scalable solution that can be adapted to monitor other crops, thereby contributing to more efficient and sustainable agricultural systems.

A novel GAN-based three-axis mutually supervised super-resolution reconstruction method for rectal cancer MR image

Background and objectiveThis study aims to enhance the resolution in the axial direction of rectal cancer magnetic resonance (MR) imaging scans to improve the accuracy of visual interpretation and quantitative analysis. MR imaging is a critical technique for the diagnosis and treatment planning of rectal cancer. However, obtaining high-resolution MR images is both time-consuming and costly. As a result, many hospitals store only a limited number of slices, often leading to low-resolution MR images, particularly in the axial plane. Given the importance of image resolution in accurate assessment, these low-resolution images frequently lack the necessary detail, posing substantial challenges for both human experts and computer-aided diagnostic systems. Image super-resolution (SR), a technique developed to enhance image resolution, was originally applied to natural images. Its success has since led to its application in various other tasks, especially in the reconstruction of low-resolution MR images. However, most existing SR methods fail to account for all anatomical planes during reconstruction, leading to unsatisfactory results when applied to rectal cancer MR images. MethodsIn this paper, we propose a GAN-based three-axis mutually supervised super-resolution reconstruction method tailored for low-resolution rectal cancer MR images. Our approach involves performing one-dimensional (1D) intra-slice SR reconstruction along the axial direction for both the sagittal and coronal planes, coupled with inter-slice SR reconstruction based on slice synthesis in the axial direction. To further enhance the accuracy of super-resolution reconstruction, we introduce a consistency supervision mechanism across the reconstruction results of different axes, promoting mutual learning between each axis. A key innovation of our method is the introduction of Depth-GAN for synthesize intermediate slices in the axial plane, incorporating depth information and leveraging Generative Adversarial Networks (GANs) for this purpose. Additionally, we enhance the accuracy of intermediate slice synthesis by employing a combination of supervised and unsupervised interactive learning techniques throughout the process. ResultsWe conducted extensive ablation studies and comparative analyses with existing methods to validate the effectiveness of our approach. On the test set from Shanxi Cancer Hospital, our method achieved a Peak Signal-to-Noise Ratio (PSNR) of 34.62 and a Structural Similarity Index (SSIM) of 96.34 %. These promising results demonstrate the superiority of our method.

LNG storage tanks 9% Ni steel weld inspection via phased-coherent TFM and SVD enhancement

ABSTRACT 9% Ni steel is commonly utilised in liquefied natural gas storage tanks due to its strong resistance to corrosion and oxidation. However, the welds in these tanks are prone to serious defects, necessitating thorough non-destructive testing. Unfortunately, when inspecting nickel-based alloy welds using phased array ultrasonic testing (PAUT) technology the ultrasonic waves are scattered and attenuated because of the coarse columnar grains. To address this challenge, this paper introduces a based on SVD enhancement directivity corrected circular coherence factor weighted TFM imaging algorithm (SVD-DC-CCF). Initially, the CCF matrix is corrected by the directivity and energy attenuation compensation factors, then weighted with the TFM pixel point amplitude. Subsequently, the weighted amplitude matrix undergoes singular value decomposition (SVD), followed by the establishment of an enhancement factor to reconstruct the amplitude matrix. The proposed algorithm is evaluated through experiments on three types of defects in a 30.0 mm thick 9% Ni steel T-shaped fillet weld test block. Results demonstrate superior signal-to-noise ratio (SNR) and enhanced image resolution. Compared to TFM results, the SNR increases by 4.97 dB, 4.25 dB, and 4.96 dB, respectively, while the array performance index (API) decreases by 9.80%, 34.39%, and 22.22%, respectively.

Resolution enhancement in terahertz imaging with multi-wavelength information

Thanks to the unique characteristics of terahertz waves, terahertz imaging has become one of the promising imaging technologies. However, due to the weak signal source and strong diffraction of terahertz waves, terahertz imaging has a significant amount of noise, which makes it is challenge to achieve satisfactory clarity in images. In this work, we propose an algorithm that uses multi-wavelength information to improve the resolution of terahertz imaging. The resolvability of the images has been improved by at least 1.4 times, and the noise has been effectively filtered out. This algorithm enhances the image resolution without requiring any hardware upgrades, benefiting the terahertz imaging system with multi-wavelength imaging capabilities.

Analysis of LST, NDVI, and UHI patterns for urban climate using Landsat-9 satellite data in Delhi

The present study is based on remote sensing techniques focusing on Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) to investigate their influence on land use and land cover dynamics, and the assessment of the Urban Heat Island (UHI) effect in Delhi, India. The objective of this study is to calculate LST, NDVI, and UHI values to understand the changes in LULC patterns, urbanization, and temperature increase within the city. Unlike previous studies conducted with Landsat-8, the present study employs Landsat-9 data, ensuring a higher level of authenticity in the results. Landsat-9, equipped with state-of-the-art sensors and instrumentation, provides superior data quality, enhanced image resolution, and advanced capabilities for precise monitoring and analysis. The methodology encompasses six steps for LST retrieval, enabling the calculation of UHI values and intensity. Ground data from 32 meteorological stations validate the LST results. Pearson correlation coefficients between LST and NDVI exhibit correlations ranging from −0.58 to −0.68 for three dates. On Dec 8, 2023, there is a weak negative correlation of −0.004. The analysis of changing land cover with variation in NDVI and LST unveils a diverse landscape, primarily characterised by green cover (47.34%), followed by built-up area (44.57%), barren land (7.57%), and water (0.52%). The study identifies the minimum value of UHI intensity for Delhi to be 8.13 °C on 26-Feb 2023 and the maximum value of UHI was estimated 10.29 °C on 2-June 2023. The study of Urban Heat Island (UHI) patterns revealed distinctive seasonal trends. The urban areas exhibited relatively cooler temperatures compared to surrounding rural regions on Dec 8, 2023. The conclusion drawn from this comprehensive analysis is that rapid urbanization in Delhi has significantly contributed to the increase in LST and UHI values. This rise can largely be attributed to the extensive use of concrete in construction activities, which exacerbates the UHI effect. Moreover, this analysis signifies the dynamic nature of UHI and emphasizes the urgency for strategic urban planning and climate-sensitive design approaches. Implementing such measures can create more sustainable and resilient urban environments.

A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification.

Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial for both trading and planting. This study collected RGB images of 20 hybrid rice seed varieties. An enhanced deep super-resolution network (EDSR) was employed to enhance image resolution, and a variety classification model utilizing the high-resolution dataset demonstrated superior performance to that of the model using the low-resolution dataset. A novel training sample selection methodology was introduced integrating deep learning with the Kennard-Stone (KS) algorithm. Convolutional neural networks (CNN) and autoencoders served as supervised and unsupervised feature extractors, respectively. The extracted feature vectors were subsequently processed by the KS algorithm to select training samples. The proposed methodologies exhibited superior performance over the random selection approach in rice variety classification, with an approximately 10.08% improvement in overall classification accuracy. Furthermore, the impact of noise on the proposed methodology was investigated by introducing noise to the images, and the proposed methodologies maintained superior performance relative to the random selection approach on the noisy image dataset. The experimental results indicate that both supervised and unsupervised learning models performed effectively as feature extractors, and the deep learning framework significantly influenced the selection of training set samples. This study presents a novel approach for training sample selection in classification tasks and suggests the potential for extending the proposed method to image datasets and other types of datasets. Further exploration of this potential is warranted. © 2024 Society of Chemical Industry.

The Development of Artificial Intelligence in Knee Joint MRI Detection

The application of artificial intelligence in the diagnosis and analysis of knee joint MRI has significantly advanced, leveraging technologies like machine learning and deep learning to enhance both accuracy and efficiency. AI models are adept at identifying, classifying, and predicting various pathologies such as osteoarthritis and ligament tears by analyzing complex imaging data. This facilitates more accurate diagnoses by assisting radiologists. Key developments include automation of image segmentation, image resolution enhancement, and noise reduction in MRI scans. Despite these advances, integrating AI into clinical workflows, managing data variability, and ensuring extensive validation pose ongoing challenges. Nonetheless, AI's potential to transform diagnostic processes, improve patient outcomes, and reduce healthcare costs by streamlining workflows remains promising.

Exit wave reconstruction of a focal series of images with structural changes in high-resolution transmission electron microscopy.

High-resolution transmission electron microscopy (HRTEM) images can capture the atomic-resolution details of the dynamically changing structure of nanomaterials. Here, we propose a new scheme and an improved reconstruction algorithm to reconstruct the exit wave function for each image in a focal series of HRTEM images to reveal structural changes. In this scheme, the wave reconstructed from the focal series of images is treated as the initial wave in the reconstruction process for each HRTEM image. Additionally, to suppress noise at the frequencies where the signal is weak due to the modulation of the lens transfer function, a weight factor is introduced in the improved reconstruction algorithm. The advantages of the new scheme and algorithms are validated by using the HRTEM images of a natural specimen and a single-layer molybdenum disulphide. This algorithm enables image resolution enhancement and lens aberration removal, while potentially allowing the visualisation of the structural evolution of nanostructures.

Research on an SICM Scanning Image Resolution Enhancement Algorithm.

Scanning ion conductance microscopy (SICM) enables the non-invasive three-dimensional imaging of live cells and other structures in physiological environments. However, when imaging complex samples, SICM faces challenges such as having a low temporal resolution during slow scanning and a reduced signal-to-noise ratio during fast scanning, making it difficult to simultaneously improve both temporal and spatial resolution. To address these issues, this paper proposes an algorithm for enhancing image resolution under high-speed scanning. Firstly, scanning images are preprocessed using a median filtering algorithm to remove the salt-and-pepper noise generated during high-speed scanning. Next, the Canny edge detection algorithm is employed to extract the edges of the image targets. To avoid blurring the edges, the new edge-directed interpolation (NEDI) algorithm is then used to fill the edges, while non-edge areas are filled using bilinear interpolation, thereby enhancing the image resolution. Finally, the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) are used to analyze the imaging of articular chondrocytes. The results show that under a scanning speed of 480 nm/ms, the proposed algorithm improves the temporal resolution of imaging by 60% compared to traditional 2× resolution imaging, increases the peak signal-to-noise ratio of the scanning images by 7 dB, and achieves a structural similarity of 0.97. Therefore, the proposed algorithm effectively removes noise during high-speed scanning and improves the SICM scanning imaging resolution, thereby avoiding the reduction in temporal resolution when scanning larger resolution samples and effectively enhancing the performance of SICM scanning imaging.

Diving head-first into brain intravital microscopy.

Tissue microenvironments during physiology and pathology are highly complex, meaning dynamic cellular activities and their interactions cannot be accurately modelled ex vivo or in vitro. In particular, tissue-specific resident cells which may function and behave differently after isolation and the heterogenous vascular beds in various organs highlight the importance of observing such processes in real-time in vivo. This challenge gave rise to intravital microscopy (IVM), which was discovered over two centuries ago. From the very early techniques of low-optical resolution brightfield microscopy, limited to transparent tissues, IVM techniques have significantly evolved in recent years. Combined with improved animal surgical preparations, modern IVM technologies have achieved significantly higher speed of image acquisition and enhanced image resolution which allow for the visualisation of biological activities within a wider variety of tissue beds. These advancements have dramatically expanded our understanding in cell migration and function, especially in organs which are not easily accessible, such as the brain. In this review, we will discuss the application of rodent IVM in neurobiology in health and disease. In particular, we will outline the capability and limitations of emerging technologies, including photoacoustic, two- and three-photon imaging for brain IVM. In addition, we will discuss the use of these technologies in the context of neuroinflammation.

Enhancing Real-Time Pyramid Holographic Display Through Iterative Algorithm Optimization for 3D Image Reconstruction

Holography, a crucial technology for 3D visualization, strives to create realistic relief images. This research aims to enhance hologram quality and viewer experience by optimizing the image-processing pipeline. Conventional holographic displays face challenges due to their bulkiness and limited viewing angles. To overcome these limitations, this study proposes a novel approach that integrates digital holography with holographic pyramid technology. Digital holography uses computer algorithms for hologram generation, while holographic pyramid technology projects images onto a reflective pyramid for 3D display. The drawback of holographic pyramid displays in low-light environments is addressed through increased diffraction to enhance image resolution. This integrated approach involves comprehensive research, including an examination of existing methods. The anticipated outcome is holograms with improved visibility and resolution from multiple angles. The research presents an initial image preprocessing phase, succeeded by sophisticated processing employing iterative algorithms. This aims to diminish the image size while upholding its quality, thereby achieving an image suitable for pyramidal display. The fusion of digital holography and holographic pyramid display shows promise for immersive visual experiences. However, advancements in processing techniques may lead to increased material complexity, posing a challenge. Through this research, the system aims to unlock creative potentials and pave the way for enhanced holographic displays in various applications.

UPAMNet: A unified network with deep knowledge priors for photoacoustic microscopy

Photoacoustic microscopy (PAM) has gained increasing popularity in biomedical imaging, providing new opportunities for tissue monitoring and characterization. With the development of deep learning techniques, convolutional neural networks have been used for PAM image resolution enhancement and denoising. However, there exist several inherent challenges for this approach. This work presents a Unified PhotoAcoustic Microscopy image reconstruction Network (UPAMNet) for both PAM image super-resolution and denoising. The proposed method takes advantage of deep image priors by incorporating three effective attention-based modules and a mixed training constraint at both pixel and perception levels. The generalization ability of the model is evaluated in details and experimental results on different PAM datasets demonstrate the superior performance of the method. Experimental results show improvements of 0.59 dB and 1.37 dB, respectively, for 1/4 and 1/16 sparse image reconstruction, and 3.9 dB for image denoising in peak signal-to-noise ratio.

Enhancing texture feature for mineral classification in tight sandstone rock thin-section images using super-resolution techniques

In petrological studies, accurately classifying minerals and pore objects in rock thin-section images is essential for understanding geological conditions of oil and gas reservoirs. A key challenge in current methodologies is the limited image resolution, which restricts the accuracy of texture feature recognition crucial for effective classification. This study introduces super-resolution (SR) techniques to overcome this limitation, aiming to enhance image resolution and thereby improve the recognition and classification of texture features in thin-section imagery. To evaluate the effectiveness of various SR methods, we conducted a series of experiments evaluating their impact on mineral type classification, employing advanced techniques such as Gabor filtering and SVM classifiers for texture feature extraction and classification.Our experiments demonstrated that all evaluated SR techniques can significantly enhance classification accuracy, with each method improving accuracy by at least 19.3% for mixed color spaces. Notably, the Enhanced Deep Super-Resolution (EDSR) model, while less intuitive for human visual assessment, emerged as highly effective in extracting intricate texture features, resulting in an approximate 24.2% increase in accuracy within multi color spaces. These results suggest that the most effective SR models for enhancing texture details are not necessarily aligned with human visual evaluation. The novelty of this work is grounded in its comprehensive assessment of SR techniques in petrological imaging, marking a substantial advancement over previous methods. This study not only enhances the discernibility of critical texture features in rock thin-section imagery but also provides a clear pathway for integrating advanced image processing techniques into petrological studies.

A deep neural network approach with attention mechanism to improve the quality of target observation for UAVs

Owing to the limitations of onboard equipment, the direct acquisition of high-resolution images in unmanned aerial vehicle (UAV) target observation tasks is a challenge. However, with the development of deep learning, super-resolution reconstruction has become a popular technique for enhancing image resolution. Though, current super-resolution reconstruction methods can enhance UAV target observation images to some extent, most of them are unable to strike a balance between the number of parameters, computational cost, and reconstruction effect. Furthermore, limited research has been conducted for overcoming the cascade coupling problem in UAV target observation images. To solve these problems and provide a lightweight, fast, and high-quality super-resolution method for UAV target observation images, an enhanced attention dense cross network (EADCN) is proposed. This method includes excellent attention mechanisms, such as the double large kernel attention module (D-LKA) and multi-aware channel attention distillation block (MAADB). In addition to benchmark datasets, a UAV target observation dataset (UAV-TOD) is created to evaluate the performance of EADCN. The experimental results demonstrate that the EADCN has fewer parameters, low computational complexity, high accuracy, and fast processing speed, making it suitable for deployment in UAVs.

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.

Viscoacoustic generalized screen propagator in constant-Q model

When seismic waves propagate through the geological formation, there is a significant loss of energy and a decrease in imaging resolution, because of the viscoacoustic properties of subsurface medium. This profoundly impacts seismic wavefield propagation, imaging and interpretation. To accurately image the true structure of subsurface medium, the consensus among geophysicists is to no longer treat subsurface medium as ideal homogeneous medium, but rather to incorporate the viscoacoustic properties of subsurface medium. Based on the generalized screen propagator using conventional acoustic wave equation (acoustic GSP), our developed method introduces viscoacoustic compensation strategy, and derives a one-way wave generalized screen propagator based on time-fractional viscoacoustic wave equation (viscoacoustic GSP). In numerical experiments, we conducted tests on two-dimensional multi-layer model and the Marmousi model. When comparing with the acoustic GSP using the acoustic data, we found that the imaging results of the viscoacoustic GSP using the viscoacoustic data showed a significant attenuation compensation effect, and achieved imaging results for both algorithms were essentially consistent. However, the imaging results of acoustic GSP using viscoacoustic data showed significant attenuation effects, especially for deep subsurface imaging. This indicates that we have proposed an effective method to compensate the attenuated seismic wavefield. Our application on a set of real seismic data demonstrated that the imaging performance of our proposed method in local areas surpassed that of the conventional acoustic GSP. This suggests that our proposed method holds practical value and can more accurately image real subsurface structures while enhancing imaging resolution compared with the conventional acoustic GSP. Finally, with respect to computational efficiency, we gathered statistics on running time to compare our proposed method with conventional Q-RTM, and it is evident that our method exhibits higher computational efficiency. In summary, our proposed viscoacoustic GSP method takes into account the true properties of the medium, still achieves migration results comparable to conventional acoustic GSP.

Spatial resolution enhancement in holographic imaging via angular spectrum expansion

Digital holography numerically restores three-dimensional image information using optically captured diffractive waves. The required bandwidth is larger than that of hologram pixel at a closer distance in the Fresnel diffraction regime, which results in the formation of aliased replica patterns in digital hologram. From the analysis of sampling phenomenon, the replica functions are revealed to be the components of higher angular spectra of hologram. A undersampled hologram consists of the moire patterns formed by the modulation of original function by complex exponential function. There is a one-to-one correspondence between the replicas in both real and Fourier spaces. The acquisition methods of high-resolution images over a wide field view are proposed on the basis of the expansion process of angular spectrum by using replicas. Only the captured hologram with low numerical aperature or low resolution restores a high-resolution image when using an optimization algorithm. Numerical simulations and optical experiments are performed to investigate the proposed scheme.

MODERN INTELLECTUAL TECHNOLOGIES IN COMPUTER TOMOGRAPHY

In this article, an approach for the enhancement of computer tomography imaging by integrating modern technologies, including photon counting and dual-source CT, along with the application of the Ai-rad principles in Siemens Computer Tomography systems, is presented. The study proposes and empirically assesses the impact of these technologies on image quality and radiation safety. The architecture of dual-source CT and the photon-counting technique are explored, showcasing their role in improving diagnostic precision. Moreover, the article elaborates on the implementation of artificial intelligence through the Ai-rad approach, aimed at optimizing the scanning process and image analysis, while ensuring radiation doses are kept as low as reasonably achievable. The advantages of these contemporary technologies in addressing current challenges in CT imaging are presented. The results indicate that the synergy between advanced hardware capabilities and intelligent software algorithms leads to enhanced image resolution, reduced exposure to radiation, and overall improved clinical outcomes. This multi-faceted approach exemplifies the potential of modern innovations to set new benchmarks in the field of medical imaging. Keywords: Dual-Source CT, Photon Counting, AI-Rad, myNeedle, Diagnostic Precision, Radiation Safety.

Optical Imaging Using Coded Aperture Correlation Holography (COACH) with PSF of Spatial-Structured Longitudinal Light Beams—A Study Review

Spatial-structured longitudinal light beams are optical fields sculpted in three-dimensional (3D) space by diffractive optical elements. These beams have been recently suggested for use in improving several imaging capabilities, such as 3D imaging, enhancing image resolution, engineering the depth of field, and sectioning 3D scenes. All these imaging tasks are performed using coded aperture correlation holography systems. Each system designed for a specific application is characterized by a point spread function of a different spatial-structured longitudinal light beam. This article reviews the topic of applying certain structured light beams for optical imaging.