Multiplicative noise, also known as speckle noise, is signal dependent and difficult to remove. Based on a fourth-order PDE model, this paper proposes a novel approach to remove the multiplicative noise on images. In practice, Fourier transform and logarithm strategy are utilized on the noisy image to convert the convolutional noise into additive noise, so that the noise can be removed by using the traditional additive noise removal algorithm in frequency domain. For noise removal, a new fourth-order PDE model is developed, which avoids the blocky effects produced by second-order PDE model and attains better edge-preserve ability. The performance of the proposed method has been evaluated on the images with both additive and multiplicative noise. Compared with some traditional methods, experimental results show that the proposed method obtains superior performance on different PSNR values and visual quality.

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additive and multiplicative noise speckle blocky effects fourthorder pde model visual quality edgepreserve fourier transform secondorder pde model approach psnr values convolutional noise frequency domain logarithm strategy methods

Wen-Sheng Chen,Bo Chen,Jin-Lin Cai,Yan Li,.A Multiplicative Noise Removal Approach Based on Partial Differential Equation Model. 2012 (),.

Wen-Sheng Chen,Bo Chen,Jin-Lin Cai,Yan Li,"A Multiplicative Noise Removal Approach Based on Partial Differential Equation Model" 2012

Wen-Sheng Chen,Bo Chen,Jin-Lin Cai,Yan Li,"A Multiplicative Noise Removal Approach Based on Partial Differential Equation Model"