Image denoising via an adaptive weighted anisotropic diffusion

Chen, Yong; He, Taoshun

doi:10.1007/s11045-020-00760-x

Cited by 8 publications

(3 citation statements)

References 36 publications

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“…Although the local spatial gradient is effective for capturing local features, it only considers the range intensity similarity of two adjacent pixels. However, it does not consider the geometric structure similarity of two adjacent regions; it, therefore, lacks robustness [ 40 ]. Inspired by the nonlocal method, in this study, an effective adaptively weighted anisotropic diffusion model was constructed.…”

Section: Proposed Denoising Algorithmmentioning

confidence: 99%

Denoising of Fluorescence Image on the Surface of Quantum Dot/Nanoporous Silicon Biosensors

Liu

Sun

Jia

et al. 2022

Sensors

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In the process of biological detection of porous silicon photonic crystals based on quantum dots, the concentration of target organisms can be indirectly measured via the change in the gray value of the fluorescence emitted from the quantum dots in the porous silicon pores before and after the biological reaction on the surface of the device. However, due to the disordered nanostructures in porous silicon and the roughness of the surface, the fluorescence images on the surface contain noise. This paper analyzes the type of noise and its influence on the gray value of fluorescent images. The change in the gray value caused by noise greatly reduces the detection sensitivity. To reduce the influence of noise on the gray value of quantum dot fluorescence images, this paper proposes a denoising method based on gray compression and nonlocal anisotropic diffusion filtering. We used the proposed method to denoise the quantum dot fluorescence image after DNA hybridization in a Bragg structure porous silicon device. The experimental results show that the sensitivity of digital image detection improved significantly after denoising.

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Section: Proposed Denoising Algorithmmentioning

confidence: 99%

Denoising of Fluorescence Image on the Surface of Quantum Dot/Nanoporous Silicon Biosensors

Liu

Sun

Jia

et al. 2022

Sensors

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“…For the enhancement of tourist street view images, related scholars have proposed a new medical tourist street view image enhancement method, which adaptively adjusts the fractional order according to the dynamic gradient of the entire tourist street view image [16]. Researchers have proposed an adaptive tourist street view image enhancement algorithm that can automatically generate fractional differential orders based on the mask window size, tourist street view image gradient, and other theories [17]. According to the local statistical information and structural characteristics of tourist street scene images, the order of fractional differential is dynamically adjusted.…”

Section: Related Workmentioning

confidence: 99%

Denoising of Tourist Street Scene Image Based on ROF Model of Second-Order Partial Differential Equation

Yang

2021

Advances in Mathematical Physics

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The noise pollution in tourist street view images is caused by various reasons. A major challenge that researchers have been facing is to find a way to effectively remove noise. Although in the past few decades people have proposed many methods of denoising tourist street scene images, the research on denoising technology of tourist street scene images is still not outdated. There is no doubt that it has become a basic and important research topic in the field of digital image processing. The evolutionary diffusion method based on partial differential equations is helpful to improve the quality of noisy tourist street scene images. This method can process tourist street scene images according to people’s expected diffusion behavior. The adaptive total variation model proposed in this paper is improved on the basis of the total variation model and the Gaussian thermal diffusion model. We analyze the classic variational PDE-based denoising model and get a unified variational PDE energy functional model. We also give a detailed analysis of the diffusion performance of the total variational model and then propose an adaptive total variational diffusion model. By improving the diffusion coefficient and introducing a curvature operator that can distinguish details such as edges, it can effectively denoise the tourist street scene image, and it also has a good effect on avoiding the step effect. Through the improvement of the ROF model, the loyalty term and regular term of the model are parameterized, the adaptive total variation denoising model of this paper is established, and a detailed analysis is carried out. The experimental results show that compared with some traditional denoising models, the model in this paper can effectively suppress the step effect in the denoising process, while protecting the texture details of the edge area of the tourist street scene image. In addition, the model in this paper is superior to traditional denoising models in terms of denoising performance and texture structure protection.

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“…Prasath et al [28] propose a spatially varying edge coherence exponentbased Tikhonov total variation for achieving similar goals as in [27]. Chen and He [29] establish a novel filtering mechanism via an adaptive weighted anisotropic diffusion model. Siddig et al [30] and Yang et al [31] study their adaptive fourth-order diffusion models to reduce the oversmoothness for image features.…”

Section: Introductionmentioning

confidence: 99%

Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

Chen

2021

Discrete Dynamics in Nature and Society

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The purpose of this paper is to develop an effective edge indicator and propose an image scale-space filter based on anisotropic diffusion equation for image denoising. We first develop an effective edge indicator named directional local variance (DLV) for detecting image features, which is anisotropic and robust and able to indicate the orientations of image features. We then combine two edge indicators (i.e., DLV and local spatial gradient) to formulate the desired image scale-space filter and incorporate the modulus of noise magnitude into the filter to trigger time-varying selective filtering. Moreover, we theoretically show that the proposed filter is robust to the outliers inherently. A series of experiments are conducted to demonstrate that the DLV metric is effective for detecting image features and the proposed filter yields promising results with higher quantitative indexes and better visual performance, which surpass those of some benchmark models.

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Image denoising via an adaptive weighted anisotropic diffusion

Cited by 8 publications

References 36 publications

Denoising of Fluorescence Image on the Surface of Quantum Dot/Nanoporous Silicon Biosensors

Denoising of Fluorescence Image on the Surface of Quantum Dot/Nanoporous Silicon Biosensors

Denoising of Tourist Street Scene Image Based on ROF Model of Second-Order Partial Differential Equation

Image Scale-Space Filtering Using Directional Local Variance Controlled Anisotropic Diffusion

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