Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors

Subspace Learning and Imputation for Streaming Big Data Matrices and Tensors Extracting latent low-dimensional structure from high-dimensional data is of paramount importance in timely inference tasks encountered with “Big Data” analytics. However, increasingly noisy, heterogeneous, and incomplete datasets, as well as the need for real-time processing of streaming data, pose major challenges to this end. In this context, the present paper permeates benefits from rank minimization to scalable imputation of missing data, via tracking low-dimensional subspaces and unraveling latent (possibly multi-way) structure from incomplete streaming data. For low-rank matrix data,…

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Classification on the Monogenic Scale Space: Application to Target Recognition in SAR Image

Classification on the Monogenic Scale Space: Application to Target Recognition in SAR Image A novel classification strategy is proposed based on the monogenic scale space for target recognition in Synthetic Aperture Radar (SAR) image. The proposed method exploits monogenic signal theory, a multidimensional generalization of the analytic signal, to capture the characteristics of SAR image, e.g., broad spectral information and simultaneous spatial localization. The components derived from the monogenic signal at different scales are then applied into a recently developed framework, sparse representation-based classification (SRC). Moreover, to deal with the…

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Outdoor Image Enhancement: Increasing Visibility Under Extreme Haze and Lighting Condition

Outdoor Image Enhancement: Increasing Visibility Under Extreme Haze and Lighting Condition   The problem of image enhancement thereby enhancement of scene visibility in outdoor images is handled. Visibility is a very important issue in case of computer based surveillance, crime analysis, driver assistance system design etc. The most important challenge related to visibility is the atmospheric haze and poor lighting. The problem becomes more challenging if haze is too dense and lighting during night is extremely poor. An automatic degradation detection and restoration algorithm has been proposed, which detects the…

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Chaotic Map Based Steganography of Gray Scale Images in Wavelet Domain

Chaotic Map Based Steganography of Gray Scale Images in Wavelet Domain Matlab Project A method of hiding the stego image in cover image by using a technique called fractional fourier transform with wavelet coefficients is proposed. Application of steganography is internet/web security. To maintain higher security Arnold transform is performed on host image with key, key is only known to a receiver/sender. For embedding, perform a fractional Fourier transform of cover image and secret image. Then apply DWT on both images. Cover image and secret image will added by using…

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Super Resolution Image Generation Using Wavelet Domain Interpolation With Edge Extraction via a Sparse Representation

Super Resolution Image Generation Using Wavelet Domain Interpolation With Edge Extraction via a Sparse Representation This letter addresses the problem of generating a super-resolution (SR) image from a single low-resolution (LR) input image in the wavelet domain. To achieve a sharper image, an intermediate stage for estimating the high-frequency (HF) subbands has been proposed. This stage includes an edge preservation procedure and mutual interpolation between the input LR image and the HF subband images, as performed via the discrete wavelet transform (DWT). Sparse mixing weights are calculated over blocks of…

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Performance assessment criteria and issues for wavelet-based image resolution enhancement technique: A short review

Performance assessment criteria and issues for wavelet-based image resolution enhancement technique: A short review Image spatial resolution enhancement using the wavelet transform is a quite classic topic in the field of image processing and there exist many algorithms, techniques and methods regarding wavelet and image resolution. Most of these approaches are based on the idea that the low resolution image is the approximation subband of a higher resolution image and try to estimate the unknown detail coefficients to rebuild the high resolution image. In this paper, we present a short…

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