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Light Field Reconstruction Using Shearlet Transform in TensorFlow
Shearlet Transform (ST) is one of the most effective approaches for light field reconstruction from Sparsely-Sampled Light Fields (SSLFs). This demo paper presents a comprehensive implementation of ST for light field reconstruction using one of the most popular machine learning libraries, i.e. Tensor Flow. The flexible architecture of TensorFlow allows
Yuan Gao 0008 +3 more
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A Comparative Study of Shearlet, Wavelet, Laplacian Pyramid, Curvelet, and Contourlet Transform to Defect Detection [PDF]
This study presents a new approach based on shearlet transform for the first time to detect damages, and compare it with the wavelet, Laplacian pyramid, curvelet, and contourlet transforms to specify different types of defects in plate structures ...
Sepideh Vafaie, Eysa Salajegheh
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Acquiring clear images is a requisite in visual sensor networks. Image enhancement is an effective way to improve image quality. In this paper, non-sub-sampled shearlet transform (NSST) multi-scale analysis is combined with phase stretch transform (PST ...
Ying Tong
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Microseismic noise suppression is widely used in the exploration of unconventional oil and gas resources. The effective microseismic downhole signals have extremely weak energy and are contaminated by strong interference, making data processing and ...
Li Han, Dongyan Wang, Pengjun Yu
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Quaternionic shearlet transform [PDF]
Abstract The shearlet transform has been shown to be a valuable and powerful time–frequency analyzing tool for optics and non-stationary signal processing. In this article, we propose a novel transform called quaternionic shearlet transform which is designed to represent quaternion-valued signals at different scales, locations and orientations.
Firdous A. Shah, Azhar Y. Tantary
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Perceptual watermarking for Discrete Shearlet transform [PDF]
This paper presents a new perceptual watermarking model for Discrete Shearlet transform (DST). DST provides the optimal representation [10] of the image features based on multi-resolution and multi-directional analysis. This property can be exploited on for watermark embedding to achieve the watermarking imperceptibility by introducing the human visual
Baharak Ahmaderaghi +3 more
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Noise reduction by adaptive-SIN filtering for retinal OCT images
Optical coherence tomography (OCT) images is widely used in ophthalmic examination, but their qualities are often affected by noises. Shearlet transform has shown its effectiveness in removing image noises because of its edge-preserving property and ...
Yan Hu +4 more
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The shearlet transform and Lizorkin spaces [PDF]
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Bartolucci, Francesca +2 more
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Low-amplitude signal detection is a key procedure in borehole microseismic and desert seismic exploration. Usually, signals are difficult to detect due to their low amplitude and noise contamination.
Li, Yue +3 more
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Bendlet Transform Based Adaptive Denoising Method for Microsection Images
Magnetic resonance imaging (MRI) plays an important role in disease diagnosis. The noise that appears in MRI images is commonly governed by a Rician distribution. The bendlets system is a second-order shearlet transform with bent elements.
Shuli Mei +5 more
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