Scanner-Dependent Threshold Estimation of Wavelet Denoising for Small-Animal PET

Zhao, Jie; Lee, Jhih-Shian; Xu, Hang; Xu, Kai; Ren, Zhenhu; Chen, Jyh-Cheng; Wu, Chung‐li

doi:10.1109/tns.2016.2616364

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…For each scale, we generate a mask by detecting the local maximum value. Then to further generate more accurate masks, we sparse the generated coefficient masks with VisuShrink threshold function [37]. The threshold θ is calculated with Eq.…”

Section: Sobel Wavalet Contourletmentioning

confidence: 99%

Neural Contourlet Network for Monocular 360° Depth Estimation

Shen

Lin

Nie

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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For a monocular 360°image, depth estimation is a challenging because the distortion increases along the latitude. To perceive the distortion, existing methods devote to designing a deep and complex network architecture. In this paper, we provide a new perspective that constructs an interpretable and sparse representation for a 360°image. Considering the importance of the geometric structure in depth estimation, we utilize the contourlet transform to capture an explicit geometric cue in the spectral domain and integrate it with an implicit cue in the spatial domain. Specifically, we propose a neural contourlet network consisting of a convolutional neural network and a contourlet transform branch. In the encoder stage, we design a spatial-spectral fusion module to effectively fuse two types of cues. Contrary to the encoder, we employ the inverse contourlet transform with learned low-pass subbands and band-pass directional subbands to compose the depth in the decoder. Experiments on the three popular 360°panoramic image datasets demonstrate that the proposed approach outperforms the state-of-the-art schemes with faster convergence. Code is available at https://github.com/ zhijieshen-bjtu/Neural-Contourlet-Network-for-MODE.

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Section: Sobel Wavalet Contourletmentioning

confidence: 99%

Neural Contourlet Network for Monocular 360° Depth Estimation

Shen

Lin

Nie

et al. 2022

IEEE Trans. Circuits Syst. Video Technol.

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“…rough the image denoising process, the interference noise can be removed so that the image quality as well as the visual effect can be better [6]. In addition, image matching, segmentation, and edge detection, as well as other processing, are based on image denoising, so the quality of image denoising determines the follow-up processing of the whole image.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Shen

Han

2021

Scientific Programming

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To explore the utilization of the convolutional neural network (CNN) and wavelet transform in ultrasonic image denoising and the influence of the optimized wavelet threshold function (WTF) algorithm on image denoising, in this exploration, first, the imaging principle of ultrasound images is studied. Due to the limitation of the principle of ultrasound imaging, the inherent speckle noise will seriously affect the quality of ultrasound images. The denoising principle of the WTF based on the wavelet transform is analyzed. Based on the traditional threshold function algorithm, the optimized WTF algorithm is proposed and applied to the simulation experiment of ultrasound images. By comparing quantitatively and qualitatively with the traditional threshold function algorithm, the advantages of the optimized WTF algorithm are analyzed. The results suggest that the image is denoised by the optimized WTF. The mean square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measurement (SSIM) of the images are 20.796 dB, 34.294 dB, and 0.672 dB, respectively. The denoising effect is better than the traditional threshold function. It can denoise the image to the maximum extent without losing the image information. In addition, in this exploration, the optimized function is applied to the actual medical image processing, and the ultrasound images of arteries and kidneys are denoised separately. It is found that the quality of the denoised image is better than that of the original image, and the extraction of effective information is more accurate. In summary, the optimized WTF algorithm can not only remove a lot of noise but also obtain better visual effect. It has important value in assisting doctors in disease diagnosis, so it can be widely applied in clinics.

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Denoising of Digital Images Using Wavelet‐Based Thresholding Techniques: A Comparison

Bhonsle¹

2021

Cognitive Behavior and Human Computer Interaction Based on Machine Learning Algorithm

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Scanner-Dependent Threshold Estimation of Wavelet Denoising for Small-Animal PET

Cited by 5 publications

References 23 publications

Neural Contourlet Network for Monocular 360° Depth Estimation

Neural Contourlet Network for Monocular 360° Depth Estimation

Deep Learning-Based Wavelet Threshold Function Optimization on Noise Reduction in Ultrasound Images

Denoising of Digital Images Using Wavelet‐Based Thresholding Techniques: A Comparison

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