2,281 research outputs found

    Single Frame Image super Resolution using Learned Directionlets

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    In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to effectively capture directional features and to extract edge information along different directions of a set of available high resolution images .This information is used as the training set for super resolving a low resolution input image and the Directionlet coefficients at finer scales of its high-resolution image are learned locally from this training set and the inverse Directionlet transform recovers the super-resolved high resolution image. The simulation results showed that the proposed approach outperforms standard interpolation techniques like Cubic spline interpolation as well as standard Wavelet-based learning, both visually and in terms of the mean squared error (mse) values. This method gives good result with aliased images also.Comment: 14 pages,6 figure

    Wavelet/shearlet hybridized neural networks for biomedical image restoration

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    Recently, new programming paradigms have emerged that combine parallelism and numerical computations with algorithmic differentiation. This approach allows for the hybridization of neural network techniques for inverse imaging problems with more traditional methods such as wavelet-based sparsity modelling techniques. The benefits are twofold: on the one hand traditional methods with well-known properties can be integrated in neural networks, either as separate layers or tightly integrated in the network, on the other hand, parameters in traditional methods can be trained end-to-end from datasets in a neural network "fashion" (e.g., using Adagrad or Adam optimizers). In this paper, we explore these hybrid neural networks in the context of shearlet-based regularization for the purpose of biomedical image restoration. Due to the reduced number of parameters, this approach seems a promising strategy especially when dealing with small training data sets

    Image interpolation using Shearlet based iterative refinement

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    This paper proposes an image interpolation algorithm exploiting sparse representation for natural images. It involves three main steps: (a) obtaining an initial estimate of the high resolution image using linear methods like FIR filtering, (b) promoting sparsity in a selected dictionary through iterative thresholding, and (c) extracting high frequency information from the approximation to refine the initial estimate. For the sparse modeling, a shearlet dictionary is chosen to yield a multiscale directional representation. The proposed algorithm is compared to several state-of-the-art methods to assess its objective as well as subjective performance. Compared to the cubic spline interpolation method, an average PSNR gain of around 0.8 dB is observed over a dataset of 200 images

    Advanced Multi-Channel SAR Imaging - Measured Data Demonstration

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    Synthetic Aperture Radar (SAR) is a well-established technique for remote sensing of the Earth. However, conventional SAR systems relying on only a single transmit and receive aperture are not capable of imaging a wide swath with high spatial resolution. Multi-channel SAR concepts, such as systems based on multiple receive apertures in azimuth, promise to overcome these restrictions, thus enabling high-resolution wide-swath imaging. Analysis revealed that these systems imperatively require sophisticated digital processing of the received signals in order to guarantee full performance independently of the spatial sample distribution imposed by the applied pulse repetition frequency (PRF). A suitable algorithm to cope with these challenges of multi-channel data is given by the “multi-channel reconstruction algorithm”, which demonstrated in comprehensive analysis and system design examples its potential for high perform-ance SAR imaging. In this context, various optimization strategies were investigated and aspects of operating multi-channel systems in burst modes such as ScanSAR or TOPS were discussed. Furthermore, a first proof-of-principle showed the algorithm’s applicability to measured multi-channel X-band data gathered by the German Aerospace Cen-ter’s (DLR) airborne F-SAR system. As a next step in the framework of multi-channel azimuth processing, this paper builds on the results recalled above and continues two paths. Firstly, focus is turned to further optimization of the proc-essing algorithm by investigating the classical Space-Time Adaptive Processing (STAP) applied to SAR. Secondly, attention is turned to the analysis of the measured multi-channel data by elaborating the impact and compensation of channel mismatch and by verifying the derived theory

    Cyclic LTI systems in digital signal processing

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    Cyclic signal processing refers to situations where all the time indices are interpreted modulo some integer L. In such cases, the frequency domain is defined as a uniform discrete grid (as in L-point DFT). This offers more freedom in theoretical as well as design aspects. While circular convolution has been the centerpiece of many algorithms in signal processing for decades, such freedom, especially from the viewpoint of linear system theory, has not been studied in the past. In this paper, we introduce the fundamentals of cyclic multirate systems and filter banks, presenting several important differences between the cyclic and noncyclic cases. Cyclic systems with allpass and paraunitary properties are studied. The paraunitary interpolation problem is introduced, and it is shown that the interpolation does not always succeed. State-space descriptions of cyclic LTI systems are introduced, and the notions of reachability and observability of state equations are revisited. It is shown that unlike in traditional linear systems, these two notions are not related to the system minimality in a simple way. Throughout the paper, a number of open problems are pointed out from the perspective of the signal processor as well as the system theorist

    Local Measurement and Reconstruction for Noisy Graph Signals

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    The emerging field of signal processing on graph plays a more and more important role in processing signals and information related to networks. Existing works have shown that under certain conditions a smooth graph signal can be uniquely reconstructed from its decimation, i.e., data associated with a subset of vertices. However, in some potential applications (e.g., sensor networks with clustering structure), the obtained data may be a combination of signals associated with several vertices, rather than the decimation. In this paper, we propose a new concept of local measurement, which is a generalization of decimation. Using the local measurements, a local-set-based method named iterative local measurement reconstruction (ILMR) is proposed to reconstruct bandlimited graph signals. It is proved that ILMR can reconstruct the original signal perfectly under certain conditions. The performance of ILMR against noise is theoretically analyzed. The optimal choice of local weights and a greedy algorithm of local set partition are given in the sense of minimizing the expected reconstruction error. Compared with decimation, the proposed local measurement sampling and reconstruction scheme is more robust in noise existing scenarios.Comment: 24 pages, 6 figures, 2 tables, journal manuscrip

    Pipelined digital SAR azimuth correlator using hybrid FFT-transversal filter

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    A synthetic aperture radar system (SAR) having a range correlator is provided with a hybrid azimuth correlator which utilizes a block-pipe-lined fast Fourier transform (FFT). The correlator has a predetermined FFT transform size with delay elements for delaying SAR range correlated data so as to embed in the Fourier transform operation a corner-turning function as the range correlated SAR data is converted from the time domain to a frequency domain. The azimuth correlator is comprised of a transversal filter to receive the SAR data in the frequency domain, a generator for range migration compensation and azimuth reference functions, and an azimuth reference multiplier for correlation of the SAR data. Following the transversal filter is a block-pipelined inverse FFT used to restore azimuth correlated data in the frequency domain to the time domain for imaging
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