The denoising and deblurring of Poisson images are opposite inverse problems. Single image deblurring methods are sensitive to image noise. A single noise filter can effectively remove noise in advance, but it also damages blurred information. To simultaneously solve the denoising and deblurring of Poissonian images better, we learn the implicit deep image prior from a single degraded image and use the denoiser as a regularization term to constrain the latent clear image. Combined with the explicit L0 regularization prior of the image, the denoising and deblurring model of the Poisson image is established. Then, the split Bregman iteration strategy is used to optimize the point spread function estimation and latent clear image estimation. The experimental results demonstrate that the proposed method achieves good restoration results on a series of simulated and real blurred images with Poisson noise.
Passive millimetre‐wave (PMMW) imaging frequently suffers from blurring and low resolution due to the long wavelengths. In addition, the observed images are inevitably disturbed by noise. Traditional image deblurring methods are sensitive to image noise, even a small amount of which will greatly reduce the quality of the point spread function (PSF) estimation. In this paper, we propose a blind deblurring and denoising method via a learning deep denoising convolutional neural networks (DnCNN) denoiser prior and an adaptive ‐regularized gradient prior for passive millimetre‐wave images. First, a blind deblurring restoration model based on the DnCNN denoising prior constraint is established. Second, an adaptive ‐regularized gradient prior is incorporated into the model to estimate the latent clear image, and the PSF is estimated in the gradient domain. In a multi‐scale framework, alternate iterative denoising and deblurring are used to obtain the final PSF estimation and noise estimation. Ultimately, the final clear image is restored by non‐blind deconvolution. The experimental results show that the algorithm used in this paper not only has good detail recovery ability but is also more stable to different noise levels. The proposed method is superior to state‐of‐the‐art methods in terms of both subjective measure and visual quality.
Background
The study aimed to explore the value of CT findings and inflammatory indicators in differentiating benign and malignant gallbladder polypoid lesions before surgery.
Methods
The study comprised a total of 113 pathologically confirmed gallbladder polypoid lesions with a maximum diameter ≥ 1 cm (68 benign and 45 malignant), all of which were enhanced CT-scanned within 1 month before surgery. The CT findings and inflammatory indicators of the patients were analyzed by univariate and multivariate logistic regression analysis to identify independent predictors of gallbladder polypoid lesions, and then a nomogram distinguishing benign and malignant gallbladder polypoid lesions was developed by combining these characteristics. The receiver operating characteristic (ROC) curve and decision curve were plotted to assess the performance of the nomogram.
Results
Base status of the lesion (p < 0.001), plain CT value (p < 0.001), neutrophil–lymphocyte ratio (NLR) (p = 0.041), and monocyte-lymphocyte ratio (MLR) (p = 0.022) were independent predictors of malignant polypoid lesions of the gallbladder. The nomogram model established by incorporating the above factors had good performance in differentiating and predicting benign and malignant gallbladder polypoid lesions (AUC = 0.964), with sensitivity and specificity of 82.4% and 97.8%, respectively. The DCA demonstrated the important clinical utility of our nomogram.
Conclusion
CT findings combined with inflammatory indicators can effectively differentiate benign and malignant gallbladder polypoid lesions before surgery, which is valuable for clinical decision-making.
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