Sub-pixel Displacement Estimation Research Articles

Research on Dual-Line Array Subpixel Scanning Imaging for IoMT-Based Blood Cell Analysis System

The Internet of Medical Things (IoMT) provides great convenience by closely connecting medical devices, doctors, and patients. In health management and disease diagnosis, the development of lensless imaging technology and microfluidics has provided important solutions for implementing the point-of-care testing (POCT) devices used to analyze blood cells. However, the low resolution is one of the significant problems, and the existing work has increased system and algorithmic complexity. We conceived an integrated sense-and-calculate chip for balance and proposed dual-line array subpixel scanning imaging (DL-SSI) for an IoMT-based blood cell acquisition and analysis device. In this article, the line-scan imaging model was derived first, and a background modeling method was proposed to eliminate systematic noise in scanned images. A subpixel displacement estimation method was proposed to calculate the cell’s transient flow velocity based on space-time compensation. The time complexity is reduced by 95.08% at typical value, and it is more friendly to the integrated sense-and-calculate chip in hardware. In the reconstruction and restoration, the interpolation theory of the scanned image was deduced, and both the autofocusing algorithm and phase iteration algorithm was optimized for the scanning reconstructed diffraction image. Finally, a lensless DL-SSI terminal was built for testing, and the theoretical minimum size is 44 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 44 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> 71.8 mm. Sufficient experiments show that DL-SSI is feasible, effective, and superior for IoMT-based blood cell acquisition and analysis.

Optical-numerical method based on a convolutional neural network for full-field subpixel displacement measurements.

The subpixel displacement estimation is an important step to calculation of the displacement between two digital images in optics and image processing. Digital image correlation (DIC) is an effective method for measuring displacement due to its high accuracy. Various DIC algorithms to compare images and to obtain displacement have been implemented. However, there are some drawbacks to DIC. It can be computationally expensive when processing a sequence of continuously deformed images. To simplify the subpixel displacement estimation and to explore a different measurement scheme, a convolutional neural network with a transfer learning based subpixel displacement measurement method (CNN-SDM) is proposed in this paper. The basic idea of the method is to compare images of an object decorated with speckle patterns before and after deformation by CNN, and thereby to achieve a coarse-to-fine subpixel displacement estimation. The proposed CNN is a classification model consisting of two convolutional neural networks in series. The results of simulated and real experiments are shown that the proposed CNN-SDM method is feasibly effective for subpixel displacement measurement due its high efficiency, robustness, simple structure and few parameters.

Position detection method of linear motor mover based on extended phase correlation algorithm

This study aims to explain the real-time and high-precision requirements of linear motor mover position detection. A new sub-pixel displacement estimation based on an extended phase correlation algorithm (PCA) is proposed and applied to measure the position of the linear motor mover. This extended PCA is on the basis of singular value decomposition (SVD), which is a novel application in position measurement of the linear motor mover. First, two adjacent stripe images are captured by a high-speed camera installed in the linear motor. Second, an improved Hanning (flap-top) window function is added to the input fence images to suppress the edge effect and spectral aliasing, before the discrete Fourier transform (DFT). Next, phase correlation matrix is acquired by PCA, and using SVD technique to calculate approximate rank-one matrix for subsequent phase unwrapping. Then, weighted-phase unwrap algorithm is applied to acquire the real horizontal phase data. Finally, least-squares method is utilised to linearly fit the real phase data and to obtain sub-pixel displacement of the linear motor mover. Experiments show that the detection accuracy is 1/100 pixel, and the position measurement of linear motor mover is fast and effective.

Computational imaging from non-uniform degradation of staggered TDI thermal infrared imager.

For the Time Delay Integration (TDI) staggered line-scanning thermal infrared imager, a Computational Imaging (CI) approach is developed to achieve higher spatial resolution images. After a thorough analysis of the causes of non-uniform image displacement and degradation for multi-channel staggered TDI arrays, the study aims to approach one-dimensional (1D) sub-pixel displacement estimation and superposition of images from time-division multiplexing scanning lines. Under the assumption that a thermal image is 2D piecewise C(2) smooth, a sparse-and-smooth deconvolution algorithm with L1-norm regularization terms combining the first and second order derivative operators is proposed to restore high frequency components and to suppress aliasing simultaneously. It is theoretically and experimentally demonstrated, with simulation and airborne thermal infrared images, that this is a state-of-the-art practical CI method to reconstruct clear images with higher frequency components from raw thermal images that are subject to instantaneous distortion and blurring.

An autocorrelation-based method for improvement of sub-pixel displacement estimation in ultrasound strain imaging

In ultrasound strain and elasticity imaging, an accurate and cost-effective sub-pixel displacement estimator is required because strain/elasticity imaging quality relies on the displacement SNR, which can often be higher if more computational resources are provided. In this paper, we introduce an autocorrelation-based method to cost-effectively improve subpixel displacement estimation quality. To quantitatively evaluate the performance of the autocorrelation method, simulated and tissue-mimicking phantom experiments were performed. The computational cost of the autocorrelation method is also discussed. The results of our study suggest the autocorrelation method can be used for a real-time elasticity imaging system.

Bias errors in subpixel displacement estimation for optical correlation-based measurement techniques

The systematic error introduced when using 1-D peak detection algorithms to decompose elliptically shaped correlation peaks is investigated. First, an analytical description of this error is derived, and it is shown that this error can lead to a systematic influence of more than one pixel. Additionally, a general linear 2-D Gaussian algorithm based on least squares estimation is presented to allow correlation peak detection of elliptically shaped peaks without any significant bias error. Finally, the performance of the presented algorithms for subpixel displacement estimation, as well as algorithms provided by commercial software packages, is tested with artificial image pairs of random dot patterns.

Global and local coordinates in digital image correlation

Digital image correlation (DIC) is commonly used to measure specimen displacements by correlating an image of a specimen in an undeformed or reference configuration and a second image under load. To establish the correlation between the images, numerical techniques are used to locate an initially square image subset in a reference image. In this process, choosing appropriate coordinates is of fundamental importance to ensure accurate results. Both global and local coordinates can be used in shape functions. However, large rigid body rotations and deformations are accurately obtained by using global rather than local shape functions. In addition, points located after displacement may not be at an integer pixel distance from the original position. Hence subpixel displacement estimation methods such as interpolation or fitting of correlation coefficients are essential. A solution using the least-squares method is employed by choosing proper coordinates, and the feasibility of using local coordinates is demonstrated and validated with a mathematical model. Both simulated and experimental results show that the proper choice of coordinates does ensure the reliability and improve the accuracy of measurements in DIC.

Sub-Pixel Estimation Error Cancellation on Area-Based Matching

Area-based image matching and sub-pixel displacement estimation using similarity measures are common methods that are used in various fields. Sub-pixel estimation using parabola fitting over three ...

Two-dimensional simultaneous subpixel estimation for area-based matching

Area-based matching is a fundamental image processing operation to obtain the displacement between images. Also, the similarity interpolation method to estimate subpixel displacement is commonly used to enhance resolution. Conventionally, similarity interpolation estimation is performed by assuming that the horizontal and vertical displacements are independent. Almost no investigations of estimation error over the conventional method have been made, but it is inferred experientially that subpixel estimation with image interpolation or gradient-based methods is more precise than the similarity interpolation method. This paper proposes a novel 2D subpixel displacement estimation method based on similarity interpolation through modeling of 2D self-similarity. The proposed method requires no “a priori” knowledge of 2D similarity and no images at all. It adopts no iteration. Furthermore, the proposed method entails only slightly higher calculation costs than the conventional similarity interpolation method. The proposed method can obtain more precise estimation than both the conventional similarity interpolation method and the image interpolation method using comparison of subpixel estimation accuracy. An experiment using actual images demonstrates the effectiveness of the proposed method. © 2005 Wiley Periodicals, Inc. Syst Comp Jpn, 36(2): 1–11, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.20221

Significance and attributes of subpixel estimation on area‐based matching

AbstractArea‐based matching and subpixel displacement estimation using similarity measures are common methods used in various fields. Subpixel estimation using parabola fitting over three points with their similarity measures is also a common method to increase matching resolution. However, such estimation can contain systematic error depending on image characteristics, the similarity function, and the fitting function used for subpixel estimation. In this paper, characteristics of subpixel estimation error were clarified using a simple analytical model. Although this model is quite simple, it allows us to make a theoretical analysis for general images without assuming any specific image pattern. Through this analysis, it has been shown that there are correct combinations of the similarity functions and the fitting functions. Also, the so‐called “pixel‐locking” effect, by which estimated positions tend to be biased toward integer values, has been explained. Finally, analyses were verified with an experiment using real images. © 2003 Wiley Periodicals, Inc. Syst Comp Jpn, 34(12): 1–10, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10506