Robust and accurate segmentation results from high resolution (HR) 3D Magnetic Resonance (MR) images are desirable in many clinical applications. State-of-the-art deep learning methods for image segmentation require external HR atlas image and label pairs for training. However, the availability of such HR labels is limited due to the annotation accuracy and the time required to manually label. In this paper, we propose a 3D label super resolution (LSR) method which does not use an external image or label from a HR atlas data and can reconstruct HR annotation labels only reliant on a LR image and corresponding label pairs. In our method, we present a Deformable U-net, which uses synthetic data with multiple deformation for training and an iterative topology check during testing, to learn a label slice evolving process. This network requires no external HR data because a deformed version of the input label slice acquired from the LR data itself is used for training. The trained Deformable U-net is then applied to through-plane slices to estimate HR label slices. The estimated HR label slices are further combined by label a fusion method to obtain the 3D HR label. Our results show significant improvement compared to competing methods, in both 2D and 3D scenarios with real data.
Interferometry is a widely used optical measurement technique. We can estimate the physical parameters of the measured object by analyzing the phase of the fringe pattern obtained by interference imaging. However, when the measurement object has spherical surface, the interferogram always contains closed fringes which the traditional analysis methods are difficult to handle. Therefore, we use several common deep learning networks to learn the closed fringe patterns and their phases, evaluate and choose the appropriate network to build an end-to-end phase analysis system for a single closed fringe pattern. The experimental results show that the constructed deep learning network model has excellent phase recovery effect on simulation closed fringe patterns, and can estimate the curvature radius of the spherical surface accurately.
Our study investigated an object-detection method based on the faster region-based convolutional neural network (faster R-CNN). The method was designed to determine the center of either a concentric circle or concentric ellipse. Specifically, the central spot of the image (as the object region) can be marked by the bounding box when the circular or elliptical image is used as input data for the faster R-CNN model. The center point of the bounding box can then be calculated according to the coordinates of the upper left and lower right corners, that is, the center position of the concentric circle or concentric ellipse. It is important to determine the center coordinates when taking optical measurements, as the curvature radius of optical components can thus be obtained. The effectiveness of this method is demonstrated through simulation images. Furthermore, we can obtain the center coordinates of the actual Newton’s rings image using the above method; according to the coordinate transformation method, the curvature radius can be estimated based on the center.
Newton’s rings are the fringe patterns of quadratic phase, the curvature radius of optical components can be obtained from the coefficients of quadratic phase. Usually, the coordinate transformation method has been used to the curvature radius, however, the first step of the algorithm is to find the center of the circular fringes. In recent years, deep learning, especially the deep convolutional neural networks (CNNs), has achieved remarkable successes in object detection task. In this work, an new approach based on the Faster region-based convolutional neural network (Faster R-CNN) is proposed to estimate the rings’ center. Once the rings’ center has been detected, the squared distance from each pixel to the rings’ center is calculated, the two-dimensional pattern is transformed into a one-dimensional signal by coordinate transformation, fast Fourier transform of the spectrum reveals the periodicity of the one-dimensional fringe profile, thus enabling the calculation of the unknown surface curvature radius. The effectiveness of this method is demonstrated by the simulation and actual images.
A method based on the fractional Fourier ridges for accurate phase demodulation of a single interferogram with quadratic phase is presented. The interferograms being analyzed may contain circular, elliptic or astigmatic fringes. In signal processing field, such interferograms can be called 2-D chirp-type signals. Since the fractional Fourier transform (FRFT) of a chirp signal is a function under the matched angle that is determined by chirp rates of the signal, so the method can be used to match the multiple chirp rates in chirp-type signals with multiple chirp components. In this work, the FRFT of all row (column) signals are firstly calculated, and the ridge of the FRFT amplitude of each row (column) signal in FRFT domain is recorded. Repeat the above process for each angle of a searching range. Then a ridge tracking approach is employed to determine the matched angle, which can be used to calculate the coefficient of the square term of row (column) coordinates. Moreover, under the matched angle, the ridge of the FRFT amplitude of each row (column) signal all lie on a straight line. The slope and constant term of the line can be used to calculate the coefficient of the linear term of row (column) coordinates and the coefficient of cross term, respectively. The same procedures are implemented to all column (row) signals to determine the coefficients of the square and liner term of column (row) coordinates. According to the obtained coefficients, the phase of the fringe pattern can be constructed without phase unwrapping operation. Furthermore, the present procedure is also capable of analysis of interferograms with or without circularly symmetry fringe distribution instead of using complex and time consuming algorithms for recovering phase from fringe patterns with closed fringes. Finally, the method is tested in simulated and real data.
Dictionary learning for sparse representation has attracted much attention among researchers in image denoising. However, most dictionary learning-based methods use a single dictionary which has limitation in sparse representation ability. To improve the performance of this methodology, we propose a multichannel color image denoising algorithm based on multiple dictionary learning. Compared with a fixed dictionary, multiple dictionaries have more powerful representation ability. The algorithm first uses a Gaussian mixture model to model the generic patch prior of an external natural color image dataset. Then, the multiple orthogonal dictionaries are initialized with the generic prior by applying singular value decomposition to the covariance matrix of each Gaussian component. The sparse coding coefficients and the multiple dictionaries are alternately updated for better fitting the desired image. Considering the difference of the noise levels in RGB channels, we use a weight matrix to adjust the contributions of different channels for the denoised result. The desired image is estimated based on maximum a posteriori framework. The extensive experiments have demonstrated that our proposed method outperforms some state-of-the-art denoising algorithms in most cases.
Fractional Fourier OFDM or simply chirped OFDM performs better in time-frequency selective channel than its convectional OFDM. Although chirped OFDM outperforms OFDM it still inherits Peak to Average Power Ratio (PAPR) drawback as a convectional OFDM. To eliminate PAPR drawback Constant Envelope OFDM was developed and for better performance in time frequency selective channel Constant Envelope Fractional Fourier OFDM (CE-COFDM) is used. Its BER performance is analyzed and compared to chirped OFDM and OFDM in AWGN and Rayleigh channel. The simulations show the BER performance of CE-COFDM is the same as chirped OFDM and OFDM. The power efficiency of CE-COFDM is also studied and different simulations performed shows CE-COFDM is more power efficient than chirped OFDM and convectional OFDM for class A and class B Linear Power Amplifier (LPA).
Spatiotemporal motion trajectory can accurately reflect the changes of motion state. Motivated by this observation, this letter proposes a method for key frame extraction based on motion trajectory on the spatiotemporal slice. Different from the well-known motion related methods, the proposed method utilizes the inflexions of the motion trajectory on the spatiotemporal slice of all the moving objects. Experimental results show that although a similar performance is achieved in the single-objective screen, by comparing the proposed method to that achieved with the state-of-the-art methods based on motion energy or acceleration, the proposed method shows a better performance in a multiobjective video.
Removal of noise is an important step in the image restoration process, and it remains a challenging problem in image processing. Denoising is a process used to remove the noise from the corrupted image, while retaining the edges and other detailed features as much as possible. Recently, denoising in the fractional domain is a hot research topic. The fractional-order anisotropic diffusion method can bring a less blocky effect and preserve edges in image denoising, a method that has received much interest in the literature. Based on this method, we propose a new method for image denoising, in which fractional-varying-order differential, rather than constant-order differential, is used. The theoretical analysis and experimental results show that compared with the state-of-the-art fractional-order anisotropic diffusion method, the proposed fractional-varying-order differential denoising model can preserve structure and texture well, while quickly removing noise, and yields good visual effects and better peak signal-to-noise ratio.
Signal reconstruction, especially for nonstationary signals, occurs in many applications such as optical astronomy, electron microscopy, and x-ray crystallography. As a potent tool to analyze the nonstationary signals, the linear canonical transform (LCT) describes the effect of quadratic phase systems on a wavefield and generalizes many optical transforms. The reconstruction of a finite discrete-time signal from the partial information of its discrete LCT and some known samples under some restrictions is presented. The partial information about its discrete LCT that we have assumed to be available is the discrete LCT phase alone or the discrete LCT magnitude alone. Besides, a reconstruction example is provided to verify the effectiveness of the proposed algorithm.
Newton’s rings pattern always blurs the scanned image when scanning a film using a film scanner. Such phenomenon is a kind of equal thickness interference, which is caused by the air layer between the film and the glass of the scanner. A lot of methods were proposed to prevent the interference, such as film holder, anti-Newton’s rings glass and emulsion direct imaging technology, etc. Those methods are expensive and lack of flexibility. In this paper, Newton’s rings pattern is proved to be a 2-D chirp signal. Then, the fractional Fourier transform, which can be understood as the chirp-based decomposition, is introduced to process Newton’s rings pattern. A digital filtering method in the fractional Fourier domain is proposed to reduce the Newton’s rings pattern. The effectiveness of the proposed method is verified by simulation. Compared with the traditional optical method, the proposed method is more flexible and low cost.
KEYWORDS: Quantization, Fringe analysis, Fourier transforms, Error analysis, Signal to noise ratio, Commercial off the shelf technology, Signal processing, Digital filtering, Image quality, Optical engineering
Newton’s rings fringe pattern is often encountered in optical measurement. The digital processing of the fringe pattern is widely used to enable automatic analysis and improve the accuracy and flexibility. Before digital processing, sampling and quantization are necessary, which introduce quantization errors in the fringe pattern. Quantization errors are always analyzed and suppressed in the Fourier transform (FT) domain. But Newton’s rings fringe pattern is demonstrated to be a two-dimensional chirp signal, and the traditional methods based on the FT domain are not efficient when suppressing quantization errors in such signals with large bandwidth as chirp signals. This paper proposes a method for suppressing quantization errors in the fractional Fourier transform (FRFT) domain, for chirp signals occupies little bandwidth in the FRFT domain. This method has better effect on reduction of quantization errors in the fringe pattern than traditional methods. As an example, a standard Newton’s rings fringe pattern is analyzed in the FRFT domain and then 8.5 dB of improvement in signal-to-quantization-noise ratio and about 1.4 bits of increase in accuracy are obtained compared to the case of the FT domain. Consequently, the image quality of Newton’s rings fringe pattern is improved, which is beneficial to optical metrology.
By manipulating the principal stretches during a two-step space mapping within the framework of transformation optics, the input spatial frequency bandwidth of a conventional Fourier lens can be extended based on an isotropic transformation material. Isotropy is important for easy fabrication, low loss, and broadband application; moreover, it suggests a route for realizing Fourier transforms of continuous fractional order with enhanced input spatial frequency bandwidth.
The continuous fractional Fourier transform (FRFT) can be interpreted as a rotation of a signal in the time-frequency plane and is a powerful tool for analyzing and processing nonstationary signals. Because of the importance of the FRFT, the discrete fractional Fourier transform (DFRFT) has recently become an important issue. We present the computation method for the DFRFT using the adaptive least-mean-square algorithm. First, the DFRFT computation scheme with single angle parameter of the signal block using the adaptive filter system is introduced. Second, considering the transform angles always change in practical applications, the DFRFT computation scheme with adjustable-angle parameter of the signal block using the adaptive filter system is presented. Then we construct two realization structures of the DFRFT computation with simultaneous multiple-angle parameters for each signal block. The proposed computation approaches have the inherent parallel structures, which make them suitable for efficient very large scale integration implementations.
In this paper, a method of multipoint location is raised based on the passive detection system and the mathematic model and error model is set up used to measure position. Analyzing a detection system relationship of minimal location fuzziness area and range, relationship of location space error, bearing angle and relationship of circular probable error of multipoint location and range and giving the simulation curve. From the result of the simulation, the location precision of this passive detection system is high. But which should be paid attention to is that the aero error and some other system error are not in the error model, in fact, those errors have a great effect on the location precision of this passive detection system. By corrections, the system error can be reduced, but which can't be removed completely.
A flexible and effective macroblock-based framework for hybrid spatial and fine-grain SNR scalable video coding is proposed in this paper. In the proposed framework, the base layer is of low resolution and is generally encoded at low bit rates with traditional prediction based coding schemes. Two enhancement layers, i.e., the low-resolution enhancement layer and the high-resolution enhancement layer, are generated to improve the video quality of the low-resolution base layer and evolve smoothly from low resolution to high resolution video with increasingly better quality, respectively. Since bit plane coding and drifting control techniques are applied to the two enhancement layers, each enhancement bitstream is fine-grain scalable and can be arbitrarily truncated to fit in the available channel bandwidth. In order to improve the coding efficiency and reduce the drifting errors at the high-resolution enhancement layer, five macroblock coding modes with different forms of motion compensation and reconstruction, are proposed in this paper. Furthermore, a mode decision algorithm is developed to select the appropriate coding mode for each macroblock at the high-resolution enhancement layer. Compared with the traditional spatial scalable coding scheme, the proposed framework not only provides the spatial scalability but also provides the fine granularity quality scalability at the same resolution.
Based on the Digital Signal Processor (ADSP21060), a principal and subordinate structure parallel real time infrared target tracking system is established. The system is composed of image acquisition sub system, image processing sub- system, manual control subsystem and target indicates subsystem. A new fast two-dimensional (2-D) entropy algorithm and motion target detection algorithm is implemented in second DSP to detect infrared target. The mass centered tracking algorithm and correlative-tracking algorithm are used to tracking targets. A Kalman filter algorithm is used to predicting approach. And using ADSP21060 to achieve the Kalman filtering algorithm and satisfies the real-time need. In this tracking system the tracking state of infrared target is considered, detecting algorithm or tracking algorithm can be selected automatic. If tracking system is working in detecting mode and target is detected, then system will turn to tracking mode .If system is working in tracking mode and target is lost, then system will turn into detecting mode. As the ADSP-21060 offers powerful features to multi-processing DSP systems, it is easy to expand to multi subordinate DSP system. It is possible to use more complexes detecting or tracing algorithms in real time tracing system.
Cell Loss Ratio estimation is a crucial technology in call admission control and traffic engineering in ATM networks. Cell arrival process and analytical approach had been investigated in recent years. However, an effective and practicable CLR estimator is still a challenge. Based on the analysis of cell loss problem, an improved simple traffic model and a new algorithm are presented in this paper, to estimate CLR of ATM network multiplexed with heterogenous traffic classes services rapidly. Traffic model is constructed with standard parameters, so it is easy to use in practical situations. The new algorithm runs quickly enough to respond the call real-time. Simulation results show that accuracy, complexity and robustness of algorithm are ideal to be utilized in real network.