Sub-pixel Registration Algorithm Research Articles

Experimental measurement of utricle dynamic response1

The utricle of the red-eared turtle was subjected to forced sinusoidal oscillations across various frequencies (10-125 Hz) and amplitudes (5-9 μm) to determine dynamic characteristics of the utricle under natural inertial stimulation. The utricle was maintained in physiologic solution during the entire experiment. Utricular specimens were prepared so that the Otoconial Layer (OL) crystals were exposed yet undisturbed, and the neuroepithelium was secured to a glass slide with dental floss strands. A piezoelectric-actuated platform, fitted to the stage of the microscope, created controlled sinusoidal displacement along the utricle's medial-lateral direction. The OL surface displacement was measured through the microscope with high-speed video at 1500 fps. A sub-pixel image registration algorithm was used to achieve displacement resolution ⩽ 15 nm. The Membranous Shelf (MS), that overlies the macula, was recorded with high-speed video under identical amplitude and frequency inputs and was used as a reference point. Maximum displacement amplitudes of the OL and MS were used to determine the Amplitude Ratio (AR) of the OL relative to the MS. ARs at various frequencies were fit to a single degree of freedom model of the utricle to determine the utricle's natural frequency of 363 Hz (95% confidence intervals: 328, 397) with a damping ratio of 0.96 (0.8, 1.12).

Digital Image Correlation with Self-Adaptive Gaussian Windows

A novel subpixel registration algorithm with Gaussian windows is put forward for accurate deformation measurement in digital image correlation technique. Based on speckle image quality and potential deformation states, this algorithm can automatically minimize the influence of subset sizes by self-adaptively tuning the Gaussian window shapes with the aid of a so-called weighted sum-of-squared difference correlation criterion. Numerical results of synthetic speckle images undergoing in-plane sinusoidal displacement fields demonstrate that the proposed algorithm can significantly improve displacement and strain measurement accuracy especially in the case with relatively large deformation.

Initial guess by improved population-based intelligent algorithms for large inter-frame deformation measurement using digital image correlation

Digital image correlation (DIC) has received a widespread research and application in experimental mechanics. In DIC, the performance of subpixel registration algorithm (e.g., Newton–Raphson method, quasi-Newton method) relies heavily on the initial guess of deformation. In the case of small inter-frame deformation, the initial guess could be found by simple search scheme, the coarse-fine search for instance. While for large inter-frame deformation, it is difficult for simple search scheme to robustly estimate displacement parameters and deformation parameters simultaneously with low computational cost. In this paper, we proposed three improving strategies, i.e. Q-stage evolutionary strategy (T), parameter control strategy (C) and space expanding strategy (E), and then combined them into three population-based intelligent algorithms (PIAs), i.e. genetic algorithm (GA), differential evolution (DE) and particle swarm optimization (PSO), and finally derived eighteen different algorithms to calculate the initial guess for qN. The eighteen algorithms were compared in three sets of experiments including large rigid body translation, finite uniaxial strain and large rigid body rotation, and the results showed the effectiveness of proposed improving strategies. Among all compared algorithms, DE-TCE is the best which is robust, convenient and efficient for large inter-frame deformation measurement.

Performance of iterative gradient-based algorithms with different intensity change models in digital image correlation

Digital image correlation (DIC) is a non-contact and powerful tool for whole-field displacement and strain measurement in modern optical metrology. In the DIC technique, the gradient-based algorithm is one of the most commonly used sub-pixel registration algorithms due to its effectiveness and accuracy. Thus, this algorithm has been further investigated and improved by many researchers to increase its accuracy. In the existing gradient-based algorithms, there are three relation models for the gray level intensity of a point in the undeformed and deformed images: namely the constant intensity model, the linear intensity change model and the non-linear intensity change model. However, little quantitative investigation has been conducted to compare their performance. In this paper, three iterative gradient-based algorithms are given using the corresponding intensity change model and the iterative least squares (ILS) solving method. The accuracy, robustness and computational efficiency of the three algorithms for intensity variation are investigated through numerical simulation experiments. The experimental results reveal that the algorithm generated using the non-linear intensity change model is the most accurate, robust and efficient of the three algorithms and the algorithms generated using the linear and non-linear intensity change models have approximately the same accuracy when the intensity variation is small between undeformed and deformed images.

Super-resolution in practice: the complete pipeline from image capture to super-resolved subimage creation using a novel frame selection method

We present a complete super-resolution system using a camera, that is assumed to be on a vibrating platform and continually capturing frames of a static scene, that have to be super-resolved in particular regions of interest. In a practical system the shutter of the camera is not synchronised with the vibrations it is subjected to. So, we propose a novel method for frame selection according to their degree of blurring and we combine a tracker with the sequence of selected frames to identify the subimages containing the region of interest. The extracted subimages are subsequently co-registered using a state of the art sub-pixel registration algorithm. Further selection of the co-registered subimages takes place, according to the confidence in the registration result. Finally, the subimage of interest is super-resolved using a state of the art super-resolution algorithm. The proposed frame selection method is of generic applicability and it is validated with the help of manual frame quality assessment.

Performance of sub-pixel registration algorithms in digital image correlation

Developments in digital image correlation in the last two decades have made it a popular and effective tool for full-field displacement and strain measurements in experimental mechanics. In digital image correlation, the use of the sub-pixel registration algorithm is regarded as the key technique to improve accuracy. Different types of sub-pixel registration algorithms have been developed. However, little quantitative research has been carried out to compare their performances. This paper investigates three types of the most commonly used sub-pixel displacement registration algorithms in terms of the registration accuracy and the computational efficiency using computer-simulated speckle images. A detailed examination of the performances of each algorithm reveals that the iterative spatial domain cross-correlation algorithm (Newton–Raphson method) is more accurate, but much slower than other algorithms, and is recommended for use in these applications.

Application of an improved subpixel registration algorithm on digital speckle correlation measurement

Digital speckle correlation method (DSCM) has been widely used in experimental mechanics to obtain the surface deformation fields. One of the challenges in practical applications is how to obtain the high accuracy with far less computation complexity. To determine the subpixel registration of the DSCM, a high efficient gradient-based algorithm is developed in this paper. The principle is described and four different modes of the algorithm are given. Based on computer-simulated images, the optimal mode of the algorithm is verified through the comparison of computation time, optimal subset-region size and sensitivity of the four modes. The influences of speckle-granule size and speckle-granule density on accuracy are studied and a quantitative estimation of the optimal speckle-granule size range is obtained. As the applications of this method, the practical deformation measurements with the rigid body translation and rotation as well as an experiment on biomechanics are presented to certify the feasibility and the validity of the algorithm.

An illumination invariant algorithm for subpixel accuracy image stabilization and its effect on MPEG-2 video compression

This paper offers a fast and simple near-closed-form solution for the least mean-squared-error (LMSE) estimation of the frame-to-frame global subpixel motion in an unsteady image sequence. The offered near-closed-form solution achieves unlimited subpixel accuracy by always employing a small and fixed number of computations, independent of the desired subpixel accuracy. The algorithm is designed so that it is insensitive to frame-to-frame intensity variations, which is a distinctive feature of the method. Experimental results demonstrate the superiority of the proposed method to the spatio-temporal differentiation and surface fitting algorithms under different illumination conditions. This paper furthermore discusses the effect of the proposed image stabilization algorithm on the performance of MPEG-2 video compression. We report that removal of global motion down to subpixel accuracy from an unsteady video improves MPEG-2 compression performance significantly (by at least 1 dB for all frame types), in spite of the fact that motion vectors are differentially encoded in MPEG-2. This result is supported via experimental results using the proposed subpixel registration algorithm and an analysis of the macroblock coding preferences accepted in MPEG-2.

A pyramid approach to subpixel registration based on intensity

We present an automatic subpixel registration algorithm that minimizes the mean square intensity difference between a reference and a test data set, which can be either images (two-dimensional) or volumes (three-dimensional). It uses an explicit spline representation of the images in conjunction with spline processing, and is based on a coarse-to-fine iterative strategy (pyramid approach). The minimization is performed according to a new variation (ML*) of the Marquardt-Levenberg algorithm for nonlinear least-square optimization. The geometric deformation model is a global three-dimensional (3-D) affine transformation that can be optionally restricted to rigid-body motion (rotation and translation), combined with isometric scaling. It also includes an optional adjustment of image contrast differences. We obtain excellent results for the registration of intramodality positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) data. We conclude that the multiresolution refinement strategy is more robust than a comparable single-stage method, being less likely to be trapped into a false local optimum. In addition, our improved version of the Marquardt-Levenberg algorithm is faster.

Temporal integration method for image-processing-based super-high-resolution image acquisition

The authors propose an image-processing-based approach towards the development of a super-high-resolution image acquisition system. Imaging methods based on this approach can be classified into two main categories: a spatial integration imaging method and a temporal integration imaging method. With regard to the spatial integration imaging method, the authors have recently presented a method for acquiring an improved-resolution image by integrating multiple images taken simultaneously with multiple different cameras. They develop their work, aiming at a particular class of application where a user indicates a region of interest (ROI) on an observed image in advance, and apply a prototypal temporal integration imaging method. The prototypal temporal integration imaging method does not involve global image segmentation, but uses a subpixel registration algorithm which describes an image warp within the ROI, with subpixel accuracy, as a deformation of quadrilateral patches. The method then performs a subpixel registration by warping an observed image with the warping function recovered from the deformed quadrilateral patches. Experimental simulations demonstrate that the temporal integration imaging is promising as a basic means of high resolution imaging.

Predicting and estimating the accuracy of a subpixel registration algorithm

It is shown that an efficient practical registration algorithm previously described by H.G. Barrow et al. (1977) can provide high-accuracy registration. Experiments with a quantized video image of a solid triangle yielded registration that was accurate to 3% of the interpixel spacing (i.e. accurate to 0.5 mils) in the x and y directions and 0.015 degrees in rotation. It may also be important to predict the accuracy in advance, to see whether specifications can be met and to estimate accuracy during registration, in order to control quality. The authors provide practical formulas for both purposes for two kinds of image point data: edge detection (ED) data and direct measurement (DM) data. In two experiments using ED data, the predicted, estimated, and observed accuracies are all in agreement. The prediction theory developed suggests five precautions to avoid loss of registration accuracy. Perhaps most important is Precaution c, the necessity of the ED case not to have a large fraction of the total segment length of the model aligned with the horizontal or vertical directions of the pixel grid. When the model consists largely of horizontal and vertical segments, a good way to observe this precaution is to tilt the pixel grid a few degrees away from perfect alignment, e.g. by tilting the video camera. A third experiment verifies that violating Precaution c can seriously degrade accuracy.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>