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3D image noise reduction and contrast enhancement in optical coherence tomography

A novel noise reduction algorithm is proposed for reducing the noise and enhancing the contrast in 3D Optical Coherence Tomography (OCT) images. First, the OCT image is divided into two subregions based on the local noise property: the background area in which the additive noise is dominant and the foreground area in which the multiplicative noise is dominant. In the background, the noise is eliminated by the 2D linear filtering combined with the frame averaging. In the foreground, the noise is eliminated by the 3D linear filtering-an extension of the 2D linear filtering. Therefore, the denoised image is reconstructed according to the combination of the denoised background and foreground. The above procedure can be formulated with a bi-linear model which can be solved efficiently. The proposed bi-linear model can dramatically improve image quality in 3D images with heavy noise and the corresponding linear filter kernel in 2D can be performed in real time. The filter kernel we used is introduced based on the linear noise model in OCT system. The noise model used in the filter kernel includes both the multiplicative (speckle) noise and the additive (incoherent) noise, where the latter is not considered in the most existing linear speckle filters and wavelet filters. Also, the filter kernel can be treated as a low pass filter and can be applied to frequency extraction. Therefore an image contrast enhancement method is introduced in the frequency domain based on the frequency decomposing and weighted combination. A set of experiments are carried out to verify the effectiveness and efficiency of the proposed algorithm.