Anisotropic Diffusion Based Multiplicative Speckle Noise Removal

Abstract

Multiplicative speckle noise removal is a challenging task in image processing. Motivated by the performance of anisotropic diffusion in additive noise removal and the structure of the standard deviation of a compressed speckle noisy image, we address this problem with anisotropic diffusion theories. Firstly, an anisotropic diffusion model based on image statistics, including information on the gradient of the image, gray levels, and noise standard deviation of the image, is proposed. Although the proposed model can effectively remove multiplicative speckle noise, it does not consider the noise at the edge during the denoising process. Hence, we decompose the divergence term in order to make the diffusion at the edge occur along the boundaries rather than perpendicular to the boundaries, and improve the model to meet our requirements. Secondly, the iteration stopping criteria based on kurtosis and correlation in view of the lack of ground truth in real image experiments, is proposed. The optimal values of the parameters in the model are obtained by learning. To improve the denoising effect, post-processing is performed. Finally, the simulation results show that the proposed model can effectively remove the speckle noise and retain minute details of the images for the real ultrasound and RGB color images.

COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning

“…During the testing phase, speckle-affected test images were used to assess filtering performance. Anisotropic diffusion filtering can preserve and enhance edge information while subduing any noise [ 24 , 47 ]. The gradient operator detects the edge information along with noise [ 48 ].…”

A Review on Performance Analysis of PDE based Anisotropic Diffusion Approaches for Image Enhancement

“…The problem of edge blurring is mildly resolved in ADMSS [41] by extending the formulation of Cottet and Ayyadi [42] to perform selective diffusion so that diffusion across the fine structures and homogeneous regions are discriminated and in this way visibility of important structures is enhanced. Fick's law based physical diffusion equation theory is used in doubly degenerate nonlinear diffusion (DDND) model by Zhou et al [43] to promote de-noising process as given below: 𝜎 𝑠𝑝𝑒𝑐𝑘𝑙𝑒 = 2,3,4 IDDND [47] 𝜎 𝑠𝑝𝑒𝑐𝑘𝑙𝑒 = 2,3,4 GAD-LBM [48] 𝑉 𝑠𝑝𝑒𝑐𝑘𝑙𝑒 = 0.03,0.12 SGS-SRAD [49] ∆ 𝑡 = 𝑉 𝑠𝑝𝑒𝑐𝑘𝑙𝑒 = 0.03,0.120.1, 𝑛 𝑖𝑡𝑒𝑟 = 100 Frost [29] Kuan [30] Gamma [31] • Successfully inhibit smoothing near edges.…”

“…where 𝛽(𝐼) = {1 − 𝑏(𝐼)} is a region indicator function. Gao et al [47] observed erroneous pixels appear in homogeneous background of the image obtained by ADMSS [41] and therefore they developed an improved DDND model (IDDND) for multiplicative speckle reduction. Xu et al [48] suggested Gabor filter based anisotropic diffusion (GAD-LBM), supporting the advantages of the Lattice Boltzmann method [81] on rapid parallel implementation.…”

“…The computation in this method (ARAD) [62] is fast but regardless of whether the edge strength of a defective object is low or high, the edge gets enhanced and therefore the applications of ARAD in medical imaging require more modifications in diffusion equation. NDEB [46] and IDDND [47] discussed in section 3.1 can be included for low contrast speckle contaminated images too. Nair et al [63] proposed first-order anisotropic diffusion (FORADF) model which is given as:…”

Restoration and enhancement of breast ultrasound images using extended complex diffusion based unsharp masking

“…The performance is evaluated in terms of PSNR and SSIM with respect to standard data. Gao et al 36 have structured anisotropic diffusion-based filter for the removal of multiplicative speckle noise. To structure the anisotropic diffusion its divergence term is decompose that helps to recognize the edges and boundary effectively.…”