Improved Phase Correlation Research Articles

A Phase Correction Model for Fourier Transform Spectroscopy

In Fourier transform spectroscopy (FTS), the conventional Mertz method is commonly used to correct phase errors of recovered spectra, but it performs poorly in correcting nonlinear phase errors. This paper proposes a phase correlation method–all-pass filter (PCM-APF) model to correct phase errors. In this model, the proposed improved phase correlation method can correct linear phase errors, and all-pass filters are applied to correct the residual nonlinear phase errors. The optimization algorithm for the digital all-pass filters employs an improved algorithm which combines the subtraction-average-based optimizer (SABO) and the golden sine algorithm (Gold-SA). The proposed PCM-APF model demonstrates high correction precision, and the optimization algorithm for the filters converges faster than traditional intelligent optimization algorithms.

Information Visualization Based on Visual Transmission and Multimedia Data Fusion

With the rapid development of information technology, the application media of visual identity design has been greatly broadened, and the requirements of dynamic variability and interactivity of vision have become higher and higher. The introduction of data as an element has brought new semantic endowments to visual design. To solve the problems of noisy image reconstruction and many outliers in traditional art design, this paper uses an improved phase correlation algorithm to reconstruct multimedia video images. In addition, based on the visual characteristics of human eyes, a multi-feature fusion viewpoint image quality evaluation algorithm is proposed. The simulation results show that the method adopted in this paper improves the uneven image texture in art design, and its real effect is greatly enhanced.

An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC

An Improved Phase Correlation Subpixel Remote Sensing Registration Algorithm Using Probability-Guided RANSAC

Seamless Mosaicking of UAV-Based Push-Broom Hyperspectral Images for Environment Monitoring

This paper proposes a systematic image mosaicking methodology to produce hyperspectral image for environment monitoring using an emerging UAV-based push-broom hyperspectral imager. The suitability of alternative methods in each step is assessed by experiments of an urban scape, a river course and a forest study area. First, the hyperspectral image strips were acquired by sequentially stitching the UAV images acquired by push-broom scanning along each flight line. Next, direct geo-referencing was applied to each image strip to get initial geo-rectified result. Then, with ground control points, the curved surface spline function was used to transform the initial geo-rectified image strips to improve their geometrical accuracy. To further remove the displacement between pairs of image strips, an improved phase correlation (IPC) and a SIFT and RANSAC-based method (SR) were used in image registration. Finally, the weighted average and the best stitching image fusion method were used to remove the spectral differences between image strips and get the seamless mosaic. Experiment results showed that as the GCPs‘ number increases, the mosaicked image‘s geometrical accuracy increases. In image registration, there exists obvious edge information that can be accurately extracted from the urban scape and river course area; comparative results can be achieved by the IPC method with less time cost. However, for the ground objects with complex texture like forest, the edges extracted from the image is prone to be inaccurate and result in the failure of the IPC method, and only the SR method can get a good result. In image fusion, the best stitching fusion method can get seamless results for all three study areas. Whereas, the weighted average fusion method was only useful in eliminating the stitching line for the river course and forest areas but failed for the urban scape area due to the spectral heterogeneity of different ground objects. For different environment monitoring applications, the proposed methodology provides a practical solution to seamlessly mosaic UAV-based push-broom hyperspectral images with high geometrical accuracy and spectral fidelity.

An improved image registration and fusion algorithm

Under the background of telemedicine, a new registration and mosaic algorithm for medical images is proposed in this paper to solve the problems of electronic noise, uneven illumination and ray scattering in the real-time medical process. The improved Retinex algorithm by trilateral filter and homomorphic filtering algorithm can enhance the image effectively in preprocessing. The improved phase correlation algorithm based on log polar transformation was used to calculate parameters, such as rotation, scaling and translation. Then, the SUSAN corner matching points were extracted in overlapping positions, the improved KD tree was used for enhancing matching efficiency. Later, matching points were purified by the improved RANSAC algorithm. Finally, images were processed by Laplacian pyramid decomposition algorithm to make the image joint seemed smooth and natural. The results of experiments and evaluation criteria confirm that the new method has high robustness in the process of medical image registration and stitching in the network.

An Improved Phase Correlation Method for Stop Detection of Autonomous Driving

Simultaneous Localization and Mapping (SLAM) is the process by which a mobile robot carrying specific sensors builds a map of the environment and at the same time uses this map to estimate its pose. Currently, SLAM has been proven its value and is a hot topic. However, challenges still exist: when the mobile robot stops in the process of motion and a large number of feature points in the environment move slightly, the feature matching process cannot eliminate the non-stationary feature point pairs. The introduction of a large number of outliers (non-stationary feature point pairs) seriously affects the observation process of SLAM. It directly leads to the estimation errors of mobile robot pose and the 3D features position, and further leads to keyframe trajectory drift. If there is a mechanism that allows the robot to accurately detect itself in a stop state, then the pose and map points could be locked, and the state variables could be optimized to make the system enter the positive succession. Therefore, detecting the stop status of the mobile robot is a significant work for SLAM. In this manuscript, an improved phase correlation method is proposed to solve the problem of stop detection for the autonomous driving vehicle in the dynamic street environment. After experiments, it is revealed that the stop detection is significant for the performance improvement to the state-of-the-art visual SLAM system, and the improved phase correlation method has higher stop detection accuracy than the conventional phase correlation in various scenarios.

Improvement of automated image stitching system for DR X-ray images

The full bone structure of X-ray images cannot be captured in a single scan with Digital radiography (DR) system. The stitching method of X-ray images is very important for scoliosis or lower limb malformation diagnosing and pre-surgical planning. Based on the image registration technology, this paper proposes a new automated image stitching method for full-spine and lower limb X-ray images. The stitching method utilized down-sampling to decrease the size of image and reduce the amount of computation; improved phase correlation algorithm was adopted to find the overlapping region; correlation coefficient was used to evaluate the similarity of overlapping region; weighted blending is brought in to produce a panorama image. The performance of the proposed method was evaluated by 40 pairs of images from patients with scoliosis or lower limb malformation. The stitching method was fully automated without any user input required. The experimental results were compared with previous methods by analyzing the same database. It is demonstrated that the improved phase correlation has higher accuracy and shorter average stitching time than previous methods. It could tackle problems including image translation, rotation and small overlapping in image stitching.

An Improved Phase Correlation Method Based on 2-D Plane Fitting and the Maximum Kernel Density Estimator

In this letter, an improved phase correlation (PC) method based on 2-D plane fitting and the maximum kernel density estimator (MKDE) is proposed, which combines the idea of Stone's method and robust estimator MKDE. The proposed PC method first utilizes a vector filter to minimize the noise errors of the phase angle matrix and then unwraps the filtered phase angle matrix by the use of the minimum cost network flow unwrapping algorithm. Afterward, the unwrapped phase angle matrix is robustly fitted via MKDE, and the slope coefficients of the 2-D plane indicate the subpixel shifts between images. The experiments revealed that the improved method can effectively avoid the impact of outliers on the phase angle matrix during the plane fitting and is robust to aliasing and noise. The matching accuracy can reach 1/50th of a pixel using simulated data. The real image sequence tracking experiment was also undertaken to demonstrate the effectiveness of the proposed PC method with a registration accuracy of root-mean-square error better than 0.1 pixels.

A New Method of Efficient Measurement for Sub-pixel Image Motion of Space Camera

In order to measure the sub-pixel image motion of space camera which is caused by satellite attitude's instability or vibration, a method for image motion detection based on combination of corner detection and improved phase correlation is proposed in this paper. Firstly, it extracts feature points from the serial images using Harris detector. Then, the corners are used to construct a binary matrix. After that, the phase correlation for the constructed image is processed to get the relative output by taking the advantage of Fourier transform symmetry. Simultaneously, Blackman window function is taken to inhibit spectrum leakage caused by truncation for the output image. Finally, the sub-motion vector is calculated with centroid of mass calculation. On the basis of the theory proposed for measuring image motion, experiments for different sub-images with different image motions are carried out. Compared with other motion vector detection technology by soft algorithm, the simulations results show that this method not only inhibits the boundary effect but also avoids bringing truncation error, which improves the accuracy of estimation of image motion. Besides, this method effectively reduces the cost of computation and storage, improving the output SNR with good unimodality and real time property. The measure accuracy is good when the sub-image size is larger than 128128. The accuracy does not change with different scene of input images. The RMS error of image motion measurement can be controlled below 0.1 pixels, which meets the requirement of the space camera. Copyright © 2015 Binary Information Press.

An Improved Phase Correlation Method for Obtaining Dynamic Feature of the Ocean from Sequential SAR Sub-aperture Images (in English)

动态特征是海洋信息中的重要方面。但是在通常的SAR 图像处理中动态信息往往会被丢失，因为这些方法大多把SAR 图像看成是观测区域的瞬时状态。实际上，我们可以从SAR 子孔径序列图像中获取动态信息，因为我们知道SAR 不同方位向孔径对应不同的成像瞬间。从序列图像中获取动态信息的一个关键步骤就是图像匹配。但是SAR 子孔径图像的强噪声特性使得传统的图像匹配算法难以奏效。该文中，为了应对SAR 子孔径图像中的噪声问题，我们提出了一种改进的相位相关法。仿真实验表明改进的算法在多数情况下都可以达到0.15 像素以上的精度以及很好的噪声鲁棒性。分析表明，该方法可以适用于从中等分辨的机载SAR 图像和高分辨的星载SAR图像中提取动态特征，速度提取精度可以达到0.15-0.3 m/s。该文将该方法用于一个实际的机载SAR 图像的处理，反演的海面动态速度在0.05-0.5 m/s 左右，这个速度范围符合海面上一般的流速范围。

A Fast Algorithm for Recognizing Translated, Rotated, Reflected, and Scaled Objects From Only Their Projections

The subject of 2-D and higher dimensional object recognition finds widespread applications in areas such as image registration and pattern recognition. Radon transform is one technique used for efficient object matching (e.g., and ). However, so far as we know, no results have been obtained that solves the recognition problem completely in the projection domain due to coupling of transform parameters. We develop a novel method for such parameter decoupling and an improved phase correlation method for accurate practical shift estimation, resulting in a fast matching algorithm based on projection data only. Simulation results show that the proposed algorithm is much faster than similar state-of-the-art approaches such as that in with comparable estimation accuracy.