605 research outputs found

    Interpolation of Low-Resolution Images for Improved Accuracy Using an ANN Quadratic Interpolator

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    The era of digital imaging has transitioned into a new one. Conversion to real-time, high-resolution images is considered vital. Interpolation is employed in order to increase the number of pixels per image, thereby enhancing spatial resolution. Interpolation's real advantage is that it can be deployed on user end devices. Despite raising the number of pixels per inch to enhances the spatial resolution, it may not improve the image's clarity, hence diminishing its quality. This strategy is designed to increase image quality by enhancing image sharpness and spatial resolution simultaneously. Proposed is an Artificial Neural Network (ANN) Quadratic Interpolator for interpolating 3-D images. This method applies Lagrange interpolating polynomial and Lagrange interpolating basis function to the parameter space using a deep neural network. The degree of the polynomial is determined by the frequency of gradient orientation events within the region of interest. By manipulating interpolation coefficients, images can be upscaled and enhanced. By mapping between low- and high-resolution images, the ANN quadratic interpolator optimizes the loss function. ANN Quadratic interpolator does a good work of reducing the amount of image artefacts that occur during the process of interpolation. The weights of the proposed ANN Quadratic interpolator are seeded by transfer learning, and the layers are trained, validated, and evaluated using a standard dataset. The proposed method outperforms a variety of cutting-edge picture interpolation algorithms.

    A novel method in adaptive image enlargement

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    Video Deinterlacing using Control Grid Interpolation Frameworks

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    abstract: Video deinterlacing is a key technique in digital video processing, particularly with the widespread usage of LCD and plasma TVs. This thesis proposes a novel spatio-temporal, non-linear video deinterlacing technique that adaptively chooses between the results from one dimensional control grid interpolation (1DCGI), vertical temporal filter (VTF) and temporal line averaging (LA). The proposed method performs better than several popular benchmarking methods in terms of both visual quality and peak signal to noise ratio (PSNR). The algorithm performs better than existing approaches like edge-based line averaging (ELA) and spatio-temporal edge-based median filtering (STELA) on fine moving edges and semi-static regions of videos, which are recognized as particularly challenging deinterlacing cases. The proposed approach also performs better than the state-of-the-art content adaptive vertical temporal filtering (CAVTF) approach. Along with the main approach several spin-off approaches are also proposed each with its own characteristics.Dissertation/ThesisM.S. Electrical Engineering 201

    Context-based adaptive image resolution upconversion

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    2009-2010 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

    Simulation and experimental verification of W-band finite frequency selective surfaces on infinite background with 3D full wave solver NSPWMLFMA

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    We present the design, processing and testing of a W-band finite by infinite and a finite by finite Grounded Frequency Selective Surfaces (FSSs) on infinite background. The 3D full wave solver Nondirective Stable Plane Wave Multilevel Fast Multipole Algorithm (NSPWMLFMA) is used to simulate the FSSs. As NSPWMLFMA solver improves the complexity matrix-vector product in an iterative solver from O(N(2)) to O(N log N) which enables the solver to simulate finite arrays with faster execution time and manageable memory requirements. The simulation results were verified by comparing them with the experimental results. The comparisons demonstrate the accuracy of the NSPWMLFMA solver. We fabricated the corresponding FSS arrays on quartz substrate with photolithographic etching techniques and characterized the vector S-parameters with a free space Millimeter Wave Vector Network Analyzer (MVNA)

    Context-based adaptive image resolution upconversion

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    A 2-D orientation-adaptive prediction filter in lifting structures for image coding

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    Cataloged from PDF version of article.Lifting-style implementations of wavelets are widely used in image coders. A two-dimensional (2-D) edge adaptive lifting structure, which is similar to Daubechies 5/3 wavelet, is presented. The 2-D prediction filter predicts the value of the next polyphase component according to an edge orientation estimator of the image. Consequently, the prediction domain is allowed to rotate ±45° in regions with diagonal gradient. The gradient estimator is computationally inexpensive with additional costs of only six subtractions per lifting instruction, and no multiplications are required. © 2006 IEEE
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