SPL Volume 26 Issue 11

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September, 2019

SPL Volume 26 Issue 11

Two-directional two-dimensional canonical correlation analysis ((2D) 2 CCA) directly seeks linear relationship between different image data sets without reshaping images into vectors. However, it fails in finding the nonlinear correlation. 

Many well-known line spectral estimators may experience significant performance loss with noisy measurements. To address the problem, we propose a deep learning denoising based approach for line spectral estimation. The proposed approach utilizes a residual learning assisted denoising convolutional neural network (DnCNN) trained to recover the unstructured noise component, which is used to denoise the original measurements.

The multiple signal classification (MUSIC) algorithmis computationally expensive in the application to joint two-dimensional (2-D) direction-of-arrival (DOA) and time-of-arrival (TOA) estimation based on uniform circular array (UCA) using orthogonal frequency-division multiplexing (OFDM) signal. This letter proposed an efficient way to compute the 3-D spatial-temporal spectrum.

Two-directional two-dimensional canonical correlation analysis ((2D) 2 CCA) directly seeks linear relationship between different image data sets without reshaping images into vectors. However, it fails in finding the nonlinear correlation.

Many well-known line spectral estimators may experience significant performance loss with noisy measurements. To address the problem, we propose a deep learning denoising based approach for line spectral estimation. The proposed approach utilizes a residual learning assisted denoising convolutional neural network (DnCNN) trained to recover the unstructured noise component, which is used to denoise the original measurements.

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