Prof. Shaoping Xu Profile

Prof. Shaoping Xu

at Nanchang Univ

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Author

Publications (5)

SPIE Journal Paper | 3 February 2023

Boosting deep image prior by integrating external and internal image priors

Shaoping Xu, Xiaohui Cheng, Jie Luo, Xiaojun Chen, Nan Xiao

JEI, Vol. 32, Issue 01, 013021, (February 2023) https://doi.org/10.1117/12.10.1117/1.JEI.32.1.013021

KEYWORDS: Image fusion, Denoising, Image quality, Education and training, Image restoration, Image processing, Image denoising, Visualization, Image enhancement, Signal to noise ratio

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Recently, an unsupervised method [denoted as deep image prior (DIP)] that interprets images as the output of a convolutional network with randomly initialized inputs has received much attention, as the DIP can adapt its network parameters to any given noisy image without requiring training pairs, providing a great flexibility for handling those noisy images with complex structures and low degree of self-similarity. However, the DIP method still suffers from a crucial limitation that only the noisy image is used as target image, which implies that only internal prior information is used for noise removal and leads to poor denoising performance, compared with supervised denoising models. In this work, we aim to boost the DIP method by integrating external and internal image priors. Specifically, with a given noisy image, we first exploited a state-of-the-art supervised method to denoise it and obtain its corresponding denoised image called initial denoised image. Then the initial denoised image containing rich external prior information was used as extra target image together with the given noisy image within the standard DIP framework, resulting in a highly effective unsupervised recovery process. Next, to take full advantage of the uncertainty of the DIP network, the above DIP denoising routine with the different random inputs was executed multiple times to generate enough complementary denoised images (samples). Finally, with an unsupervised weight map generative network, the generated samples were fused in a pixel-wise manner. The fused image with better image quality was treated as the final denoised image. We had verified the denoising performance of our method on a large of images from several benchmark databases as well as real-world noisy images. The comparative results show that our method outperforms the original DIP method and other unsupervised methods by a large margin, and surpasses state-of-the-art supervised counterparts with comparable peak signal-to-noise rate. Source code is publicly available at https://github.com/chenxiaojun0101/BDIP.

SPIE Journal Paper | 15 October 2018

Convolutional neural network-based detector for random-valued impulse noise

Shaoping Xu, Guizhen Zhang, Lingyan Hu, Tingyun Liu

JEI, Vol. 27, Issue 05, 050501, (October 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.5.050501

KEYWORDS: Sensors, Detection and tracking algorithms, Roads, Denoising, Neural networks, Convolutional neural networks, Statistical modeling, Binary data, Digital filtering, Visual process modeling

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SPIE Journal Paper | 1 February 2018

Fast noise level estimation algorithm based on principal component analysis transform and nonlinear rectification

Shaoping Xu, Xiaoxia Zeng, Yinnan Jiang, Yiling Tang

JEI, Vol. 27, Issue 01, 010501, (February 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.1.010501

KEYWORDS: Principal component analysis, Image processing, Interference (communication), Statistical analysis, Algorithm development, Error analysis, Databases, Computer science, Information technology, Image denoising

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SPIE Journal Paper | 15 February 2016

Quality-aware features-based noise level estimator for block matching and three-dimensional filtering algorithm

Shaoping Xu, Lingyan Hu, Xiaohui Yang

JEI, Vol. 25, Issue 01, 013029, (February 2016) https://doi.org/10.1117/12.10.1117/1.JEI.25.1.013029

KEYWORDS: Denoising, 3D image processing, Visualization, Image filtering, Image quality, Image processing, Interference (communication), Image denoising, Feature extraction, Algorithm development

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The performance of conventional denoising algorithms is usually controlled by one or several parameters whose optimal settings depend on the contents of the processed images and the characteristics of the noises. Among these parameters, noise level is a fundamental parameter that is always assumed to be known by most of the existing denoising algorithms (so-called nonblind denoising algorithms), which largely limits the applicability of these nonblind denoising algorithms in many applications. Moreover, these nonblind algorithms do not always achieve the best denoised images in visual quality even when fed with the actual noise level parameter. To address these shortcomings, in this paper we propose a new quality-aware features-based noise level estimator (NLE), which consists of quality-aware features extraction and optimal noise level parameter prediction. First, considering that image local contrast features convey important structural information that is closely related to image perceptual quality, we utilize the marginal statistics of two local contrast operators, i.e., the gradient magnitude and the Laplacian of Gaussian (LOG), to extract quality-aware features. The proposed quality-aware features have very low computational complexity, making them well suited for time-constrained applications. Then we propose a learning-based framework where the noise level parameter is estimated based on the quality-aware features. Based on the proposed NLE, we develop a blind block matching and three-dimensional filtering (BBM3D) denoising algorithm which is capable of effectively removing additive white Gaussian noise, even coupled with impulse noise. The noise level parameter of the BBM3D algorithm is automatically tuned according to the quality-aware features, guaranteeing the best performance. As such, the classical block matching and three-dimensional algorithm can be transformed into a blind one in an unsupervised manner. Experimental results demonstrate that the performance of the BBM3D algorithm is superior to its nonblind version both in visual qualitative evaluations and quantitative comparisons for various images and noise levels.

SPIE Journal Paper | 23 April 2015

Adaptive image contrast enhancement algorithm for point-based rendering

Shaoping Xu, Xiaoping P. Liu

JEI, Vol. 24, Issue 02, 023033, (April 2015) https://doi.org/10.1117/12.10.1117/1.JEI.24.2.023033

KEYWORDS: Image enhancement, Image quality, Image contrast enhancement, Visualization, Distortion, Computer simulations, Volume rendering, Tissues, Image processing, Virtual reality

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