This work aims to explore the restoration of images corrupted by impulse noise via distribution-transformed network (DTN), which utilizes convolutional neural network to learn pixel-distribution features from noisy im...This work aims to explore the restoration of images corrupted by impulse noise via distribution-transformed network (DTN), which utilizes convolutional neural network to learn pixel-distribution features from noisy images. Compared with the traditional median-based algorithms, it avoids the complicated pre-processing procedure and directly tackles the original image. Additionally, different from the traditional methods utilizing the spatial neighbor information around the pixels or patches and optimizing in an iterative manner, this work turns to capture the pixel-level distribution information by means of wide and transformed network learning. DTN fits the distribution at pixel-level with larger receptions and more channels. Furthermore, DTN utilities a residual block without batch normalization layer to generate a good estimate. In terms of edge preservation and noise suppression, the proposed DTN consistently achieves significantly superior performance than current state-of-the-art methods, particularly at extreme noise densities.展开更多
Recently, exploiting low rank property of the data accomplished by the non-convex optimization has shown great potential to decrease measurements for compressed sensing. In this paper, the low rank regularization is a...Recently, exploiting low rank property of the data accomplished by the non-convex optimization has shown great potential to decrease measurements for compressed sensing. In this paper, the low rank regularization is adopted to gradient similarity minimization, and applied for highly undersampled magnetic resonance imaging(MRI) reconstruction, termed gradient-based low rank MRI reconstruction(GLRMRI). In the proposed method,by incorporating the spatially adaptive iterative singular-value thresholding(SAIST) to optimize our gradient scheme, the deterministic annealing iterates the procedure efficiently and superior reconstruction performance is achieved. Extensive experimental results have consistently demonstrated that GLRMRI recovers both realvalued MR images and complex-valued MR data accurately, especially in the edge preserving perspective, and outperforms the current state-of-the-art approaches in terms of higher peak signal to noise ratio(PSNR) and lower high-frequency error norm(HFEN) values.展开更多
基金the National Natural Science Founding of China (Nos. 61362001, 61362009 and 61661031)the Jiangxi Advanced Project for Post-Doctoral Research Fund (No. 2014KY02)+1 种基金the Young and Key Scientist Training Plan of Jiangxi Province (Nos. 20162BCB23019, 20171BBH80023 and GJJ170566)the Fund for Postgraduate of Nanchang University (No. CX2018144)。
文摘This work aims to explore the restoration of images corrupted by impulse noise via distribution-transformed network (DTN), which utilizes convolutional neural network to learn pixel-distribution features from noisy images. Compared with the traditional median-based algorithms, it avoids the complicated pre-processing procedure and directly tackles the original image. Additionally, different from the traditional methods utilizing the spatial neighbor information around the pixels or patches and optimizing in an iterative manner, this work turns to capture the pixel-level distribution information by means of wide and transformed network learning. DTN fits the distribution at pixel-level with larger receptions and more channels. Furthermore, DTN utilities a residual block without batch normalization layer to generate a good estimate. In terms of edge preservation and noise suppression, the proposed DTN consistently achieves significantly superior performance than current state-of-the-art methods, particularly at extreme noise densities.
基金the National Natural Science Foundation of China(Nos.61362001,61503176,61661031)Jiangxi Advanced Project for Post-Doctoral Research Fund(No.2014KY02)
文摘Recently, exploiting low rank property of the data accomplished by the non-convex optimization has shown great potential to decrease measurements for compressed sensing. In this paper, the low rank regularization is adopted to gradient similarity minimization, and applied for highly undersampled magnetic resonance imaging(MRI) reconstruction, termed gradient-based low rank MRI reconstruction(GLRMRI). In the proposed method,by incorporating the spatially adaptive iterative singular-value thresholding(SAIST) to optimize our gradient scheme, the deterministic annealing iterates the procedure efficiently and superior reconstruction performance is achieved. Extensive experimental results have consistently demonstrated that GLRMRI recovers both realvalued MR images and complex-valued MR data accurately, especially in the edge preserving perspective, and outperforms the current state-of-the-art approaches in terms of higher peak signal to noise ratio(PSNR) and lower high-frequency error norm(HFEN) values.
