To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhoo...
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To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with Gaussian-Sinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54% on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability
Label efficiency has become an increasingly important objective in deep learning applications. Active learning aims to reduce the number of labeled examples needed to train deep networks, but the empirical performance...
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Objectives The sense of smell is important as a warning system, in social communication and in guiding food intake. Impairment is common, and cases are increasing following COVID-19. Olfactory dysfunction may lead to ...
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Objectives The sense of smell is important as a warning system, in social communication and in guiding food intake. Impairment is common, and cases are increasing following COVID-19. Olfactory dysfunction may lead to decreased quality of life. There are several established ways to assess olfaction including the "Sniffin' Sticks" which are a validated test for healthy and diseased populations. Methods The odor threshold is traditionally determined using a single staircase procedure, with narrow or wide step. We investigated a Bayesian adaptive algorithm (QUEST) to estimate olfactory threshold in a hyposmic population compared with a healthy control group. Thresholds were measured using the three procedures in two sessions (Test and Retest). Results All the tested methods showed considerable overlap in both groups: there was a positive correlation between the QUEST procedure and classic staircase method (r = 0.88), and high test-retest reliability for all three methods used (Sniffin' Sticks narrow: r = 0.81;Sniffin' Sticks wide: r = 0.95;QUEST: r = 0.80). Conclusions Results from these approaches exhibit considerable overlap with all of them being suitable for clinical use. An advantage of the QUEST method can be the defined number of trials needed to determine an odor threshold.
The Huge Object model is a distribution testing model in which we are given access to independent samples from an unknown distribution over the set of strings {0, 1}n, but are only allowed to query a few bits from the...
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The multi-armed bandit (MAB) models have attracted significant research attention due to their applicability and effectiveness in various real-world scenarios such as resource allocation, online advertising, and dynam...
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In this paper, an adaptive algorithm for filtering speckle noise in Division-of-Focal-Plane (DoFP) polarization images is presented. The proposed algorithm involves replacing the first round of operation in the Block-...
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In this paper we present and analyze a weighted residual a posteriori error estimate for an optimal control problem. The problem involves a nondifferentiable cost functional, a state equation with an integral fraction...
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In adaptive systems, the least mean square error (LMS) adaptive algorithm is widely used. The variable step size LMS algorithm (SVSLMS) based on the Sigmoid function has greatly improved the convergence speed, and the...
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Promoting healthy lifestyle behaviors remains a major public health concern, particularly due to their crucial role in preventing chronic conditions such as cancer, heart disease, and type 2 diabetes. Mobile health ap...
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