1. Articles from Min Xu

    1-2 of 2
    1. Selective retinex enhancement based on the clustering algorithm and block-matching 3D for optical coherence tomography images

      Selective retinex enhancement based on the clustering algorithm and block-matching 3D for optical coherence tomography images

      It is important to enhance the contrast and remove the speckle noise for optical coherence tomography (OCT) images. In this paper, we propose a selective retinex enhancement method based on the fuzzy c-means (FCM) clustering algorithm to enhance only the structure part in OCT images and combines with the block-matching 3D (BM3D) algorithm for filtering. In the proposed selective retinex enhancement method, we first calculate the feature image of the original image, which includes the mean value and standard deviation of each pixel in the original image and its correlation image. Second, by applying the FCM clustering algorithm to the ...

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    2. Texture preservation and speckle reduction in optical coherence tomography using the shearlet-based total variation algorithm

      Texture preservation and speckle reduction in optical coherence tomography using the shearlet-based total variation algorithm

      In optical coherence tomography (OCT), speckle residue, texture loss, and edge blur are the main factors that limit the image quality improvement during speckle reduction, especially for human retinal OCT images. This paper proposes a shearlet-based total variation de-speckling framework (STV method) to address this problem. This method includes a de-speckling regularisation term, an edge-preserving regularisation term, and a fidelity term. By assigning a suitable weight to each term, speckle reduction, texture protection, and edge preservation can be adjusted to an optimal match. Based on the split Bregman iteration, a fast numerical algorithm for this method is also proposed. We ...

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    1-2 of 2
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