Image interpolation using Shearlet based iterative refinement

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

Development of control algorithm for a new 12s-6p single phase field excited flux switching motor

Flux switching motor (FSM) fall into a special category of switch reluctance motors (SRM). One of the key features of FSM is its rotor structure. Generally, it is free from any magnet and winding. Thus, allowing the motor to attain considerably higher speed and more stability then conventional AC motor. However, this simple and robust structure demands more sophisticated driving mechanism mainly due to the absence of rotating magneto motive force (MMF) in the rotor. The main concern of this research is to design algorithms for starting and driving 12 slots and 6 poles (12S-6P) segmental rotor field excited flux switching motor (FEFSM) and evaluate the algorithms efficiency by analyzing motor’s dynamic performance in terms of torque and current consumption. In this research, two algorithms have been proposed in which first algorithm is based on bipolar DC signals while second algorithm is based on field oriented control (FOC) principle. For position detection, algorithms merely need a basic infrared transceiver sensor. Bipolar DC signal algorithm is based on changing the polarity of armature DC voltage on the detection of zero rotor position. On the other hand, FOC algorithm involves detection of rotor zero position to estimate speed and prediction of instantaneous rotor position in real time. Initially, fundamental control principle for 12S-6P FEFSM has been identified through the finite element analysis (FEA) of the model. Afterwards control algorithms have been successfully developed and implemented in the motor control hardware. Compared to Bi-polar DC algorithm, the observations shows that the single phase FOC algorithm results in far less distortion of armature voltage waveforms even at high speed, which results in jittering free motor operation. On the other hand, Bi-polar DC algorithm results in much higher torque production, which is about 50% more than that of the single phase FOC’s yield. In terms of simulation and prototype performance comparison, Bi-polar DC algorithm is about 92% efficient in torque generation in case of initial model of FEFSM and staggering efficiency around 96% in case of optimized motor model

A Novel Rate Control Algorithm for Onboard Predictive Coding of Multispectral and Hyperspectral Images

Predictive coding is attractive for compression onboard of spacecrafts thanks to its low computational complexity, modest memory requirements and the ability to accurately control quality on a pixel-by-pixel basis. Traditionally, predictive compression focused on the lossless and near-lossless modes of operation where the maximum error can be bounded but the rate of the compressed image is variable. Rate control is considered a challenging problem for predictive encoders due to the dependencies between quantization and prediction in the feedback loop, and the lack of a signal representation that packs the signal's energy into few coefficients. In this paper, we show that it is possible to design a rate control scheme intended for onboard implementation. In particular, we propose a general framework to select quantizers in each spatial and spectral region of an image so as to achieve the desired target rate while minimizing distortion. The rate control algorithm allows to achieve lossy, near-lossless compression, and any in-between type of compression, e.g., lossy compression with a near-lossless constraint. While this framework is independent of the specific predictor used, in order to show its performance, in this paper we tailor it to the predictor adopted by the CCSDS-123 lossless compression standard, obtaining an extension that allows to perform lossless, near-lossless and lossy compression in a single package. We show that the rate controller has excellent performance in terms of accuracy in the output rate, rate-distortion characteristics and is extremely competitive with respect to state-of-the-art transform coding

Regularity scalable image coding based on wavelet singularity detection

In this paper, we propose an adaptive algorithm for scalable wavelet image coding, which is based on the general feature, the regularity, of images. In pattern recognition or computer vision, regularity of images is estimated from the oriented wavelet coefficients and quantified by the Lipschitz exponents. To estimate the Lipschitz exponents, evaluating the interscale evolution of the wavelet transform modulus sum (WTMS) over the directional cone of influence was proven to be a better approach than tracing the wavelet transform modulus maxima (WTMM). This is because the irregular sampling nature of the WTMM complicates the reconstruction process. Moreover, examples were found to show that the WTMM representation cannot uniquely characterize a signal. It implies that the reconstruction of signal from its WTMM may not be consistently stable. Furthermore, the WTMM approach requires much more computational effort. Therefore, we use the WTMS approach to estimate the regularity of images from the separable wavelet transformed coefficients. Since we do not concern about the localization issue, we allow the decimation to occur when we evaluate the interscale evolution. After the regularity is estimated, this information is utilized in our proposed adaptive regularity scalable wavelet image coding algorithm. This algorithm can be simply embedded into any wavelet image coders, so it is compatible with the existing scalable coding techniques, such as the resolution scalable and signal-to-noise ratio (SNR) scalable coding techniques, without changing the bitstream format, but provides more scalable levels with higher peak signal-to-noise ratios (PSNRs) and lower bit rates. In comparison to the other feature-based wavelet scalable coding algorithms, the proposed algorithm outperforms them in terms of visual perception, computational complexity and coding efficienc

Graph Spectral Image Processing

Recent advent of graph signal processing (GSP) has spurred intensive studies of signals that live naturally on irregular data kernels described by graphs (e.g., social networks, wireless sensor networks). Though a digital image contains pixels that reside on a regularly sampled 2D grid, if one can design an appropriate underlying graph connecting pixels with weights that reflect the image structure, then one can interpret the image (or image patch) as a signal on a graph, and apply GSP tools for processing and analysis of the signal in graph spectral domain. In this article, we overview recent graph spectral techniques in GSP specifically for image / video processing. The topics covered include image compression, image restoration, image filtering and image segmentation