Two-dimensional Edge Detection Research Articles

Tunable optical spatial differential operation via photonic spin Hall effect in a Weyl semimetal.

Optical differential operation is the basic principle of optical image edge detection, which has the advantages of high efficiency, simple structure and markerless compared with the traditional digital image processing methods. In this paper, we propose an optical differential operation with high contrast based on the photonic spin Hall effect in a Weyl semimetal, which enables to switch between one- and two-dimensional edge detection. Due to the unique optical and electrical properties of the Weyl semimetal, a transport model for the differential operation is established, which is closely related to the beam shifts. By tuning the incidence conditions, we effectively manipulate the in-plane and transverse shifts to switch differential operations between one and two dimensions. The contrast of the differential operation is further regulated by changing the physical parameters of the Weyl semimetal, and can be improved by two orders of magnitude compared to the conventional differentiator. This study provides new possibilities in edge detection and image processing owing to the advantages of switchable dimension and high contrast.

Wideband optical edge detection based on dielectric metasurface

Optical image edge detection manifests itself as the advantages of fast speed, low energy consumption, parallel operation, and large information capacity, playing a crucial role in object and image recognition in the realms of biology, medicine, and artificial intelligence. In this paper, a dielectric metasurface that can achieve object edge detection in the full visible band is proposed. By combining two optical differential methods referring to spatial spectrum-dependent transmission function and 4f system-based spatial spectrum phase shift, a tetratomic macropixel metasurface that enables polarization-correlated interference based on dual geometric phase modulation is designed, which can achieve object edge detection under 473, 530, and 633 nm wavelengths, and even under white light conditions. This coherent filtering metasurface operator provides a flexible approach for achieving wideband two-dimensional edge detection.

Electrically tunable optical spatial differentiation with graphene.

In recent years, optical analog computing has experienced rapid development, among which optical differential operation has attracted great attention. Here, based on the unique optical properties of graphene, we propose an electrically tunable optical spatial differentiation by introducing a graphene layer at a quartz substrate. It is found that the output light field is sensitive to the graphene layer near the Brewster angle for small polarization output at the graphene-quartz substrate interface and can be modulated by changing the Fermi energy of graphene. In this case, the result of the optical differential operation can be dynamically regulated. Almost strict one-dimensional differential operations in different directions and almost perfect two-dimensional differential operations can be achieved. In addition, two-dimensional edge detection with different degrees of distortion in different directions can also be realized when applied to image processing. This new modulation method may provide more possibilities for tunable image edge detection and provide a potential way for developing more versatile optical simulators in the future.

Optical edge detection with adjustable resolution based on cascaded Pancharatnam-Berry lenses.

We designed a versatile optical edge detection setup with two cascaded Pancharatnam-Berry lenses (PBLs) placed at the Fourier plane of a 4f system. When the two PBLs are parallel and close to each other, owing to the moiré-like effect, one-dimensional edge detection with adjustable resolution is achieved by introducing a transverse displacement of one PBL. Furthermore, two-dimensional edge detection with adjustable resolution can also be realized by tuning the longitudinal distance between the PBLs, and the transverse displacement is exploited to adjust the edge resolution in specified directions. The proposed scheme is verified by a proof-of-principle experiment in which the resolution-adjustable edges of different targets and cells were clearly observed, showing its flexibility and potential application in image processing and high-contrast microscopy.

Realizing depth measurement and edge detection based on a single metasurface

Abstract How to simultaneously obtain the depth, edge, and other light information of the scene to accurately perceive the physical world is an important issue for imaging systems. However, such tasks usually require bulky optical components and active illumination methods. Here, we design and experimentally validate a single geometric metasurface that can achieve depth measurement or edge detection under incoherent or coherent light respectively. Double helix point source function is utilized, and three verification experiments are carried out, including double-helix beam calibration, 2D object and 3D object detection, respectively. Additionally, two-dimensional edge detection can also be achieved. This compact imaging system can enable new applications in various fields, from machine vision to microscopy.

P‐12.1: Two‐dimensional Edge Detection Implementation of Liquid Crystal Computing Metasurfaces

Computing metasurfaces have received widespread attention since they were proposed, and the ability to realize mathematical operations has enabled real time image processing with new breakthroughs, such as optical edge detection. In this paper, a new method for two-dimensional edge detection is presented, and the two-dimensional liquid crystal polarization grating is designed from the theoretical derivation. The gratings with different periods are fabricated by photoalignment technology. The one-dimensional and two-dimensional imaged edge detection are experimental realized in this work. By measuring the displacement of edge detection, the experimental results with different periods are in line with theoretical formula. The low cost and easily fabricated photoaligned liquid crystal polarization gratings achieved the real time optical computation with broadband and adjustable, which can be further applied in biological image edge and other optical display related fields.

Optical analog computing of two-dimensional spatial differentiation based on the Brewster effect.

Optical analog computing has attracted widespread attention in recent decades due to its advantages of lower consumption, higher efficiency, and real-time imaging in image processing. Here, we propose a two-dimensional optical analog computing scheme based on the Brewster effect. We experimentally demonstrate two-dimensional edge detection with high efficiency. By combining microscopy, our approach may develop some significant applications in cellular and molecular imaging.

Two-dimensional optical edge detection based on Pancharatnam-Berry phase metasurface

With the rapid development of metasurface and metamaterials, the image edge detection based on the optical spatial differential calculation becomes an interesting topic in recent years. There have been a certain number of studies in this region, but most of them are applicable only to one-dimensional optical spatial differential calculation. In this work, a two-dimensional optical differentiator using Pancharatnam-Berry (P-B) phase metasurface is proposed and implemented in optical image two-dimensional edge detection. Based on the principle of the spin-dependent splitting from P-B phase devices, this metasurface is capable of separating the left-handed circularly polarized light from the right-handed circularly polarized light at a certain spatial distance. After filtering out the overlapped linear polarization, the left optical information is the result of the two-dimensional optical spatial differential. Meanwhile, the resolution of the image edge information is adjustable by changing the optic axis distribution of this two-dimensional optical differentiator. These results indicate that our P-B phase metasurface can be applied to the extraction of the optical image two-dimensional edge information, and the extracted edge information is more complete than the previous one-dimensional grating metasurface. For these advantages, this two-dimensional optical differentiator shows great potential applications in ultrafast optical calculation and image processing.

Two-Dimensional Edge Detection by Guided Mode Resonant Metasurface

In this letter, a new approach to perform edge detection is presented using an all-dielectric CMOS-compatible metasurface. The design is based on guided-mode resonance which provides a high quality factor resonance to make the edge detection experimentally realizable. The proposed structure that is easy to fabricate, can be exploited for detection of edges in two dimensions due to its symmetry. Also, the trade-off between gain and resolution of edge detection is discussed which can be adjusted by appropriate design parameters. The proposed edge detector has also the potential to be used in ultrafast analog computing and image processing.

A subpixel edge detection method based on an arctangent edge model

The sub-pixel edge detection method is widely applied in image processing to improve accuracy of measurement and recognition. Detection methods often encounter difficulties with low computational efficiency or poor robustness. To address such difficulties, a new least-squared-error-based method is proposed in this paper. First, a one-dimensional solution is derived by means of an arctangent edge model. In the two-dimensional situation, the Sobel operator and the cubic surface fitting method are used to determine the normal direction of edge. Then, two-dimensional edge detection can be transformed into a one-dimensional problem that can be solved with a one-dimensional solution. Because there is no complicated surface fitting in this least-squared-error-based method, it will provide an opportunity to ascertain quickly the accurate location of an edge. The experiment is described at the end of the paper, comparing three edge detection methods. The results indicate that the new detection approach has robustness equal to the traditional least-squared-error-based methods, while run time is much faster and very close to the moment-based methods. The above advantages indicate this approach is very suitable for on-line accurate detection.

Edge detection based on gradient ghost imaging.

We present an experimental demonstration of edge detection based on ghost imaging (GI) in the gradient domain. Through modification of a random light field, gradient GI (GGI) can directly give the edge of an object without needing the original image. As edges of real objects are usually sparser than the original objects, the signal-to-noise ratio (SNR) of the edge detection result will be dramatically enhanced, especially for large-area, high-transmittance objects. In this study, we experimentally perform one- and two-dimensional edge detection with a double-slit based on GI and GGI. The use of GGI improves the SNR significantly in both cases. Gray-scale objects are also studied by the use of simulation. The special advantages of GI will make the edge detection based on GGI be valuable in real applications.

A Sub-Pixel Edge Detection Method Combining the Interpolation with the Moment Theory

A new sub-pixel edge detection method is proposed to improve the detection accuracy. Firstly, using the theory of interpolation to acquire the continuous gray level distribution in one-dimensional .Therefore, the location of edge is determined. Secondly, in view of the two-dimensional edge detection, the moment spatial is taken into account. At last, the two-dimensional edge detection simplified as one-dimensional. From the test ,its known that the accuracy of the this algorithm is higher, especially for images with noise. So, the proposed algorithm has good applicability in image processing.

Enhancement of SNR and Spatial Resolution in $\varphi$-OTDR System by Using Two-Dimensional Edge Detection Method

A two-dimensional edge detection method has been proposed to extract location information of intruder in the distributed vibration sensing system based on phase-sensitive optical time domain reflectometry. The edge detection method is used to calculate the spatial gradient of the image composed by Rayleigh traces at each point by convolving with Sobel operator, hence the amplitude fluctuation of Rayleigh backscattering traces induced by external vibration can be located. The signal to noise ratio of location information based on the method increases to as high as 8.4 dB compared to conventional method, where the effects of noise are reduced by local averaging within the neighborhood of mask. The spatial resolution could be also optimized from 5 m to ~3 m when 50 ns pulse is launched into the single mode fiber with 1 Km length. The sensing system has the potential to extract available signals from the hostile environments with strong background noise.

Automated visual inspection for surface appearance defects of varistors using an adaptive neuro-fuzzy inference system

The purpose of this study is to develop an automated visual inspection system for analysis of the surface appearance of ring varistors based on an adaptive neuro-fuzzy inference system (ANFIS). Known image patterns of the six types of ring varistors are used in a training process to establish Sugeno FIS rules, and the input-output data are then set to train the ANFIS to tune the membership function. Feature extraction reduces image complexity using two-dimensional edge detection, calculated within divided rectangular region. The ANFIS combines the neural network adaptive capabilities and fuzzy logic qualitative to train a classification system for six different types of components. The performance of the ANFIS is evaluated in terms of training performance and classification accuracy. The results confirm that the proposed ANFIS is capable of classifying the six types of ring varistors with an accuracy of 98.67%.

A three-dimensional second-derivative surface-detection algorithm for volume determination on SPECT images

Most existing techniques for determining volumes on single-photon-emission computed tomography (SPECT) data employ thresholding, two-dimensional edge detection, or manual delineation of edges. These methods, however, are limited in both accuracy and applicability. In seeking to overcome these limitations, a truly three-dimensional (3D) second-derivative-based algorithm which can be implemented with relative ease has been developed. The method incorporates 3D matrix operators; these are convoluted with the SPECT count data in order to produce a 3D voxel map whose data elements correspond to the second derivative of counts in the image. This map is then searched, a suitable derivative-based edge-defining criterion being applied to each voxel position, in order to locate the derivative surface boundary which defines the volume. Validation is obtained using phantom data from 99Tcm-filled bottles of volumes 200, 580 and 2500 cm3 placed within a body-sized tank containing background activities set to give a range of contrasts between 1.00 and 0.75 (i.e. background 0% to 25%).

Automatic vessel tracking and measurement for Doppler studies

In this initial report, an imaging technique is described for automatically recognising the walls of a blood vessel during Doppler measurements, and hence automatically adjusting the position of the sample volume. The method reduces the inherently two-dimensional edge detection problem to a one-dimensional one, and iterates over several imaging frames to optimise the information. The orientation and diameter of the vessel are also measured. The technique has been clinically tested off-line and is particularly applicable to Doppler volumetric flow studies.

Demonstration of a c.c.d. image processor for two-dimensional edge detection

An analog technique for image processing that provides 2-dimensional edge detection is described. The algorithm implemented is the Sobel operator using a 3 × 3 array of picture elements. The implementation is unique in that a fully integrated circuit approach is employed which has the potential of being integrated directly into either an optical or infrared sensor. The n channel charge-coupled device (c.c.d.) technology used results in a processor area of less than 0.06 cm2, and can provide an increase in processing speeds of several orders of magnitude over general-purpose machines. Typical results from this processor are given and are compared with computer simulation.