The SURE-LET approach using hybrid thresholding function for image denoising

Zhang, Xiaobo

doi:10.1016/j.compeleceng.2016.07.010

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…SURE-LET solves for the optimal weights of the denoising results of different shrinkage basis functions using a system of linear equations, which can be used for denoising a signal, image, video, etc. [27,28].…”

Section: Sure-letmentioning

confidence: 99%

Hyperspectral Prediction Model of Nitrogen Content in Citrus Leaves Based on the CEEMDAN–SR Algorithm

Gao,

Tang,

et al. 2023

Remote Sensing

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Nitrogen content is one of the essential elements in citrus leaves (CL), and many studies have been conducted to determine the nutrient content in CL using hyperspectral technology. To address the key problem that the conventional spectral data-denoising algorithms directly discard high-frequency signals, resulting in missing effective signals, this study proposes a denoising preprocessing algorithm, complete ensemble empirical mode decomposition with adaptive noise joint sparse representation (CEEMDAN–SR), for CL hyperspectral data. For this purpose, 225 sets of fresh CL were collected at the Institute of Fruit Tree Research of the Guangdong Academy of Agricultural Sciences, to measure their elemental nitrogen content and the corresponding hyperspectral data. First, the spectral data were preprocessed using CEEMDAN–SR, Stein’s unbiased risk estimate and the linear expansion of thresholds (SURE–LET), sparse representation (SR), Savitzky–Golay (SG), and the first derivative (FD). Second, feature extraction was carried out using principal component analysis (PCA), uninformative variables elimination (UVE), and the competitive adaptive re-weighted sampling (CARS) algorithm. Finally, partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR) were used to construct a CL nitrogen prediction model. The results showed that most of the prediction models preprocessed using the CEEMDAN–SR algorithm had better accuracy and robustness. The prediction models based on CEEMDAN–SR preprocessing, PCA feature extraction, and GPR modeling had an R2 of 0.944, NRMSE of 0.057, and RPD of 4.219. The study showed that the CEEMDAN–SR algorithm can be effectively used to denoise CL hyperspectral data and reduce the loss of effective information. The prediction model using the CEEMDAN–SR+PCA+GPR algorithm could accurately obtain the nitrogen content of CL and provide a reference for the accurate fertilization of citrus trees.

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Section: Sure-letmentioning

confidence: 99%

Hyperspectral Prediction Model of Nitrogen Content in Citrus Leaves Based on the CEEMDAN–SR Algorithm

Gao,

Tang,

et al. 2023

Remote Sensing

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“…One of the most important steps in the de-noising development process was finding the optimal threshold. Encouraged by results in recent studies [33][34][35][36][37], we tested six thresholding functions ( Figure 2) to de-noise the original MODIS-NPP images in this study. Taking a MODIS-NPP image from 9 January 2013 as an example, the signal-to-noise ratios (SNRs) and root mean square difference (RMSDs) from de-noising the image using the six thresholds varied from 9.02 to 17.20 Mg ha -1 a -1 and from 0.38 to 0.99, respectively ( Table 1).…”

Section: The Discrete Wavelet Transform For De-noising Modis-npp Imagesmentioning

confidence: 99%

Prediction of High-Quality MODIS-NPP Product Data

Liu

Wang

et al. 2019

Remote Sensing

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Net primary productivity (NPP) is a key vegetation parameter and ecological indicator for tracking natural environmental change. High-quality Moderate Resolution Imaging Spectroradiometer Net primary productivity (MODIS-NPP) products are critical for assuring the scientific rigor of NPP analyses. However, obtaining high-quality MODIS-NPP products consistently is challenged by factors such as cloud contamination, heavy aerosol pollution, and atmospheric variability. This paper proposes a method combining the discrete wavelet transform (DWT) with an extended Kalman filter (EKF) for generating high-quality MODIS-NPP data. In this method, the DWT is used to remove noise in the original MODIS-NPP data, and the EKF is applied to the de-noised images. The de-noised images are modeled as a triply modulated cosine function that predicts the NPP data values when excessive cloudiness is present. This study was conducted in South China. By comparing measured NPP data to original MODIS-NPP and NPP estimates derived from combining the DWT and EKF, we found that the accuracy of the NPP estimates was significantly improved. The MODIS-NPP estimates had a mean relative error (RE) of 13.96% and relative root mean square error (rRMSE) of 15.67%, while the original MODIS-NPP had a mean RE of 23.58% and an rRMSE of 24.98%. The method combining DWT and EKF provides a feasible approach for generating new, high-quality NPP data in the absence of high-quality original MODIS-NPP data.

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“…Thierry Bluet al [28], introduced a novel method to de-noise the image, based on the image-domain and minimizing an estimate of mean squared error. Zhang et al [29], has proposed a new hybrid thresholding function to restore the original image by merging local wiener filter with pointwise thresholding function. Xiaobo Zhang et al [30], improved the SURE-LET method by using wiener filters and suggested an enhanced inter-scale based SURE-LET for better de-noising performance by combining the wiener estimator and the inter-scale based SURE-LET thresholding function.…”

Section: Iistein's Unbiased Risk Estimate (Sure) Shrinkmentioning

confidence: 99%

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

2019

IJITEE

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Images are often affected by different kinds of noise while acquiring, storing and transmitting it. Even the datasets gathered by the various image acquiring devices would be contaminated by noise. Hence, there is a need for noise reduction in the image, often called Image De-noising and thereby it becomes the significant concerns and fundamental step in the area of image processing. During image de-noising, the big challenge before the researchers is removing noise from the original image in such a way that most significant properties like edges, lines, etc., of the image, should be preserved. There were various published algorithms and techniques to de-noise the image and every single approach has its own limitations, benefits, and assumptions. This paper reviews the noise models and presents a comparative analysis of various de-noising filters that works for color images with single and mixed noises. It also suggests the best filter for color that involve in producing a high-quality color image. The metrics like PSNR, Entropy, SSIM, MSE, FSIM, and EPI are considered as image quality assessment metrics.

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The SURE-LET approach using hybrid thresholding function for image denoising

Cited by 5 publications

References 19 publications

Hyperspectral Prediction Model of Nitrogen Content in Citrus Leaves Based on the CEEMDAN–SR Algorithm

Hyperspectral Prediction Model of Nitrogen Content in Citrus Leaves Based on the CEEMDAN–SR Algorithm

Prediction of High-Quality MODIS-NPP Product Data

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

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