Presence Of Specular Reflections Research Articles

Enhanced Radar Detection in the Presence of Specular Reflection Using a Single Transmitting Antenna and Three Receiving Antennas

Radar target echoes undergo fading in the presence of specular reflection, which is adverse to radar detection. To address this problem, this paper proposes a radar detection method that uses a single transmitting antenna and three receiving antennas. The proposed method uses the maximum absolute value of the difference in the radar received signal power among the three receiving antennas as the test statistic. First, the target echo in the presence of specular reflection is analyzed. Then, selection of the required number of radar antennas and the heights at which they must be situated are discussed. Subsequently, analytical expressions of the radar detection probability and the false alarm probability are derived. Finally, simulation results are presented, which show that the proposed method improves radar detection performance in the presence of specular reflection.

Single-pixel imaging in the presence of specular reflections

Single-pixel imaging (SPI), which uses a photodetector to detect the reflected total light intensity of a set of structured illumination patterns modulated by a target scene, provides a method for visible waveband imaging, hyperspectral imaging, and terahertz imaging. However, it faces a challenge when the scene to be imaged has specular reflections. To deal with this problem, a multi-angle method without feature matching is presented. With this method, the location of the detector does not affect image reconstruction, and the results of reconstruction at each location are matched at the pixel level automatically. In simulations, with the original image as a reference, the structural similarity index value of the picture obtained by the proposed method is 10% higher than the picture obtained from a single angle. The signal-to-noise ratio value of the picture obtained by the proposed method is 4.424, which is higher than 1.577 of the maximum value of the reconstruction result from a single angle. To evaluate the method, a metal key and an aircraft engine blade with specular reflections are taken as the target scene and are reconstructed from four different imaging perspectives, giving results that are matched at the pixel level. The final reconstructed image is obtained using the principal component analysis algorithm or the fourth-order partial differential equations and principal component analysis algorithm. Compared with the image obtained from a single angle, the correlation coefficient between the image obtained by the proposed method and the reference image is increased from the minimum value of 0.3139 to 0.7050, and the power ratio is increased from 4.52% to 73.63%. The proposed method has great potential specifically for improving the quality of SPI for scenes exhibiting specular reflections.

EasySpec: Automatic Specular Reflection Detection and Suppression From Endoscopic Images

The outcome of endoscopic tasks can be significantly affected by the presence of specular reflections. Although numerous methods have been proposed for specular reflection detection and suppression from endoscopic images, they are inefficient, usually require tedious empirical parameters selection, and perform poorly when handling large specular regions. To this end, we propose a robust and efficient deep learning framework termed EasySpec for identifying and suppressing specular reflections from endoscopic images. Our proposed EasySpec consists of two stages: a detection stage and a suppression stage. The former stage is performed using a lightweight UNet-variant dubbed Scaled-UNet, which is trained exploiting a novel hybrid strategy that integrates the advantages of both transfer learning and weakly supervised techniques. The latter stage is achieved utilizing the concept of deep image inpainting. Specifically, a new and fast end-to-end approach for inpainting multi-size specular reflection regions in endoscopic images is developed. Our proposed approach termed GatedResUNet, takes advantage of the gated convolution to accurately differentiate specular pixels from non-specular pixels, and the U-Net architecture to learn multi-level semantic representative features, which helps reconstruct more realistic and semantically plausible images. Extensive qualitative and quantitative experiments on several endoscopic datasets acquired from different MIS scenes reveal that our proposed EasySpec outperforms state-of-the-art endoscopic specular reflection suppression approaches. Specifically, EasySpec generalizes well on various MIS scenes, and can properly reconstruct blood vessels and fine details in a reasonable processing time, which validates its practical significance.

A fast specular reflection removal based on pixels properties method

Iris recognition has been around for many years due to an extensive research on the uniqueness of human iris. It is well known that the iris is not similar to each other which means every human in the planet has their own iris pattern and cannot be shared. One of the main issues in iris recognition is iris segmentation. One element that can reduce the accuracy of iris segmentation is the presence of specular reflection. Another issue is the speed of specular reflection removal since the iris recognition system needs to process a lot of irises. In this paper, a specular reflection removal method was proposed to achieve a fast and accurate specular reflection removal. Some modifications were implemented on the existing pixels properties method. Based on the results, the proposed method achieved the fastest execution time, the highest segmentation accuracy and the highest SSIM compared to the other methods. This proves that the proposed method is fast and accurate to be implemented in the iris recognition systems.

Kinematic artifacts in the subsurface-offset extended image and their elimination by a dip-domain specularity filter

Common-image gathers in the dip-angle domain may be computed in relation to wave-equation migration methods, extended by the subsurface offset. They involve the application of a postmigration local Radon transform on the subsurface-offset extended image. In the dip-angle domain, seismic reflections are focused around the specular dip angle of reflection. This focusing distinguishes them from any other event in the image space. We have incorporated the dip-angle information about the presence of specular reflections into the computation of the conventional scattering-angle-dependent reflection coefficient. We have designed a specularity filter in the dip-angle domain based on a local semblance formula that recognizes and passes events associated with specular reflections, while suppressing other sorts of nonspecular signal. The filter is remarkably effective at eliminating either random or coherent noises that contaminates the prestack image. In particular, our dip-angle filter provides a method for the suppression of kinematic artifacts, commonly generated by migration in the subsurface-offset domain. These artifacts are due to an abrupt truncation of the data acquisition geometry on the recording surface. We have studied their appearance and devised an appropriate formation mechanism in the subsurface-offset and scattering-angle domains. The prominent presence of the kinematic artifacts in image gathers usually impairs the quality of the postmigration analysis and decelerates the convergence of wave-equation inversion techniques. We have determined from testing on synthetic and field data that using the proposed dip-angle-domain specularity filter efficiently eliminates the kinematic artifacts in the delivered gathers. We expect involvement of the specularity filter to increase the reliability and quality of the seismic processing chain and provide a faster convergence of iterative methods for seismic inversion.

Accuracy of Carotid Strain Estimates From Ultrasonic Wall Tracking: A Study Based on Multiphysics Simulations and In Vivo Data

We used a multiphysics model to assess the accuracy of carotid strain estimates derived from a 1-D ultrasonic wall tracking algorithm. The presented tool integrates fluid-structure interaction (FSI) simulations with an ultrasound simulator (Field II), which allows comparison of the ultrasound (US) images with a ground truth. Field II represents tissue as random points on which US waves reflect and whose position can be updated based on the flow field and vessel wall deformation from FSI. We simulated the RF-signal of a patient-specific carotid bifurcation, including the blood pool as well as the vessel wall and surrounding tissue. Distension estimates were obtained from a wall tracking algorithm using tracking points at various depths within the wall, and further processed to assess radial and circumferential strain. The simulated data demonstrated that circumferential strain can be estimated with reasonable accuracy (especially for the common carotid artery and at the lumen-intima and media-adventitia interface), but the technique does not allow to reliably assess intra-arterial radial strain. These findings were supported by in vivo data of 10 healthy adults, showing similar circumferential and radial strain profiles throughout the arterial wall. We concluded that these deviations are present due to the complex 3-D vessel wall deformation, the presence of specular reflections and, to a lesser extent, the spatially varying beam profile, with the error depending on the phase in the cardiac cycle and the scanning location.

Measuring the voidage of a CFB through image analysis

The error associated with estimating the local concentration of particles observed in digital videos of the inner wall of a circulating fluidized bed (CFB) riser is evaluated. Understanding the dynamics of these rapidly moving particles will allow researchers to design cleaner and more efficient CFB facilities. However, the seemingly random motion exhibited by the particles in three dimensions, coupled with the varying image quality, make it difficult to extract the required information from the images. A method has been developed for automatically detecting particles from a video sequence. By exploiting the presence of specular reflections, individual particles are identified along the focal plane by an image filter specifically designed for this purpose. An evaluation of the proposed method indicates its potential to estimate particle count, location, and concentration in an efficient and reliable manner.

Stereo Image Analysis of Non-Lambertian Surfaces

Stereo image analysis is based on establishing correspondences between a pair of images by determining similarity measures for potentially corresponding image parts. Such similarity criteria are only strictly valid for surfaces with Lambertian (diffuse) reflectance characteristics. Specular reflections are viewpoint dependent and may thus cause large intensity differences at corresponding image points. In the presence of specular reflections, traditional stereo approaches are often unable to establish correspondences at all, or the inferred disparity values tend to be inaccurate, or the established correspondences do not belong to the same physical surface point. The stereo image analysis framework for non-Lambertian surfaces presented in this contribution combines geometric cues with photometric and polarimetric information into an iterative scheme that allows to establish stereo correspondences in accordance with the specular reflectance behaviour and at the same time to determine the surface gradient field based on the known photometric and polarimetric reflectance properties. The described approach yields a dense 3D reconstruction of the surface which is consistent with all observed geometric and photopolarimetric data. Initially, a sparse 3D point cloud of the surface is computed by traditional blockmatching stereo. Subsequently, a dense 3D profile of the surface is determined in the coordinate system of camera 1 based on the shape from photopolarimetric reflectance and depth technique. A synthetic image of the surface is rendered in the coordinate system of camera 2 using the illumination direction and reflectance properties of the surface material. Point correspondences between the rendered image and the observed image of camera 2 are established with the blockmatching technique. This procedure yields an increased number of 3D points of higher accuracy, compared to the initial 3D point cloud. The improved 3D point cloud is used to compute a refined dense 3D surface profile. These steps are iterated until convergence of the 3D reconstruction. An experimental evaluation of our method is provided for areas of several square centimetres of forged and cast iron objects with rough surfaces displaying both diffuse and significant specular reflectance components, where traditional stereo image analysis largely fails. A comparison to independently measured ground truth data reveals that the root-mean-square error of the 3D reconstruction results is typically of the order 30---100 μm at a lateral pixel resolution of 86 μm. For two example surfaces, the number of stereo correspondences established by the specular stereo algorithm is several orders of magnitude higher than the initial number of 3D points. For one example surface, the number of stereo correspondences decreases by a factor of about two, but the 3D point cloud obtained with the specular stereo method is less noisy, contains a negligible number of outliers, and shows significantly more surface detail than the initial 3D point cloud. For poorly known reflectance parameters we observe a graceful degradation of the accuracy of 3D reconstruction.

Retrieving multiple light sources in the presence of specular reflections and texture

Recovering multiple point light sources from a sparse set of photographs in which objects of unknown texture can move is challenging. This is because both diffuse and specular reflections appear to slide across surfaces, which is a well known physical fact. What is seldom demonstrated, however, is that it can be taken advantage of to address the light source recovery problem. In this paper, we therefore show that, if approximate 3D models of the moving objects are available or can be computed from the images, we can solve the problem without any a priori constraints on the number of sources, on their color, or on the surface albedos. Our approach involves finding local maxima in individual images, checking them for consistency across images, retaining the apparently specular ones, and having them vote in a Hough-like scheme for potential light source directions. The precise directions of the sources and their relative power are then obtained by optimizing a standard lighting model. As a byproduct we also obtain an estimate of various material parameters such as the unlighted texture and specular properties. We show that the resulting algorithm can operate in presence of arbitrary textures and an unknown number of light sources of possibly different unknown colors. We also estimate its accuracy using ground-truth data.

Monopulse tracking with phased array search radar in the presence of specular reflection from sea surface [Correspondence

Extraction of elevation information with phased array search radar in the presence of specular reflection from sea surface is presented. Specular reflection from sea causes large peak errors in the measurement of low elevation angle. An algorithm based on complex indicated angle is derived to reduce specular reflection component. From the complex indicated angle and the exactly known or approximately known data, the authors can generate a specular reflection error finding function (SREFF). SREFF clearly indicates where the measurements are much affected by the specular reflection in the complex indicated angle data.

Separating reflection components based on chromaticity and noise analysis

Many algorithms in computer vision assume diffuse only reflections and deem specular reflections to be outliers. However, in the real world, the presence of specular reflections is inevitable since there are many dielectric inhomogeneous objects which have both diffuse and specular reflections. To resolve this problem, we present a method to separate the two reflection components. The method is principally based on the distribution of specular and diffuse points in a two-dimensional maximum chromaticity-intensity space. We found that, by utilizing the space and known illumination color, the problem of reflection component separation can be simplified into the problem of identifying diffuse maximum chromaticity. To be able to identify the diffuse maximum chromaticity correctly, an analysis of the noise is required since most real images suffer from it. Unlike existing methods, the proposed method can separate the reflection components robustly for any kind of surface roughness and light direction.

Stereo and Specular Reflection

The problem of accurate depth estimation using stereo in the presence of specular reflection is addressed. Specular reflection, a fundamental and ubiquitous reflection mechanism, is viewpoint dependent and can cause large intensity differences at corresponding points, resulting in significant depth errors. We analyze the physics of specular reflection and the geometry of stereopsis which lead to a relationship between stereo vergence, surface roughness, and the likelihood of a correct match. Given a lower bound on surface roughness, an optimal binocular stereo configuration can be determined which maximizes precision in depth estimation despite specular reflection. However, surface roughness is difficult to estimate in unstructured environments. Therefore, trinocular configurations, independent of surface roughness are determined such that at each scene point visible to all sensors, at least one stereo pair can produce correct depth. We have developed a simple algorithm to reconstruct depth from the multiple stereo pairs.

Error Analysis of Surface Normals Determined by Radiometry

Surface normals can be computed from three images of a workpiece taken under three distinct lighting conditions without requiring surface continuity. Radiometric methods are susceptible to systematic errors such as: errors in the measurement of light source orientations; mismatched light source irradiance; detector nonlinearity; the presence of specular reflection or shadows; the spatial and spectral distribution of incident light; surface size, material, and microstructure; and the length and properties of the light source to target path. Typically, a 1° error in surface orientation of a Lambertian workpiece is caused by a 1 percent change in image intensity due to variations in incident light intensity or a 1° change in orientation of a collimated light source. Tests on a white nylon sphere indicate that by using modest error prevention and calibration schemes, surface angles off the camera axis can be computed within 5°, except at edge pixels. Equations for the sensitivity of surface normals to major error sources have been derived. Results of surface normal estimation and edge extraction experiments on various non-Lambertian and textured workpieces are also presented.