Sharp Oracle Inequalities for High-Dimensional Matrix Prediction

Stéphane Gaiffas; Guillaume Lecue

doi:10.1109/tit.2011.2136318

Sharp Oracle Inequalities for High-Dimensional Matrix Prediction

IEEE Transactions on Information Theory ◽

10.1109/tit.2011.2136318 ◽

2011 ◽

Vol 57 (10) ◽

pp. 6942-6957 ◽

Cited By ~ 4

Author(s):

Stéphane Gaiffas ◽

Guillaume Lecue

Keyword(s):

High Dimensional ◽

Oracle Inequalities ◽

Dimensional Matrix

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Non-asymptotic oracle inequalities for the high-dimensional cox regression via lasso

Statistica Sinica ◽

10.5705/ss.2012.240 ◽

2013 ◽

Cited By ~ 1

Author(s):

Shengchun Kong ◽

Bin Nan

Keyword(s):

Cox Regression ◽

High Dimensional ◽

Oracle Inequalities

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Modified Rider Optimization-Based V Channel Magnification for Enhanced Video Super Resolution

International Journal of Image and Graphics ◽

10.1142/s0219467821500030 ◽

2021 ◽

pp. 2150003

Author(s):

Rohita H. Jagdale ◽

Sanjeevani K. Shah

Keyword(s):

Spline Interpolation ◽

Super Resolution ◽

Window Size ◽

Cubic Spline ◽

Motion Blur ◽

Video Frame ◽

High Dimensional ◽

Cubic Spline Interpolation ◽

Dimensional Matrix ◽

Video Frames

In video Super Resolution (SR), the problem of cost expense concerning the attainment of enhanced spatial resolution, computational complexity and difficulties in motion blur makes video SR a complex task. Moreover, maintaining temporal consistency is crucial to achieving an efficient and robust video SR model. This paper plans to develop an intelligent SR model for video frames. Initially, the video frames in RGB format will be transformed into HSV. In general, the improvement in video frames is done in V-channel to achieve High-Resolution (HR) videos. In order to enhance the RGB pixels, the current window size is enhanced to high-dimensional window size. As a novelty, this paper intends to formulate a high-dimensional matrix with enriched pixel intensity in V-channel to produce enhanced HR video frames. Estimating the enriched pixels in the high-dimensional matrix is complex, however in this paper, it is dealt in a significant way by means of a certain process: (i) motion estimation (ii) cubic spline interpolation and deblurring or sharpening. As the main contribution, the cubic spline interpolation process is enhanced via optimization in terms of selecting the optimal resolution factor and different cubic spline parameters. For optimal tuning, this paper introduces a new modified algorithm, which is the modification of the Rider Optimization Algorithm (ROA) named Mean Fitness-ROA (MF-ROA). Once the HR image is attained, it combines the HSV and converts to RGB, which obtains the enhanced output RGB video frame. Finally, the performance of the proposed work is compared over other state-of-the-art models with respect to BRISQUE, SDME and ESSIM measures, and proves its superiority over other models.

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