However, most of existing speckle reduction filters concentrate the blurring near features and introduced the hole artifacts, making the subsequent processing procedures complicated. Optimization-based methods can globally distribute such blurring, leading to better feature preservation. Motivated by this, we propose a novel optimization framework based on L0minimization for feature preserving ultrasound speckle reduction. We observed that the GAP, which integrates gradient and phase information, is extremely sparser in despeckled images than in speckled images. Based on this observation, we propose the L0minimization framework to remove speckle noise and simultaneously preserve features in ultrasound images. It seeks for the L0sparsity of the GAP values, and such sparsity is achieved by reducing small GAP values to zero in an iterative manner. Since features have larger GAP magnitudes than speckle noise, the proposed L0minimization is capable of effectively suppressing the speckle noise. Meanwhile, the rest of GAP values corresponding to prominent features are kept unchanged, leading to better preservation of those features. In addition, we propose an efficient and robust numerical scheme to transform the original intractable L0minimization into several sub-optimizations, from which we can quickly find their closed-form solutions. Experiments on synthetic and clinical ultrasound images demonstrate that our approach outperforms other state-of-the-art despeckling methods in terms of noise removal and feature preservation.
|Title of host publication||Computer Vision - 13th Asian Conference on Computer Vision, ACCV 2016, Revised Selected Papers|
|Number of pages||16|
|Publication status||Published - 1 Jan 2017|
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
- Theoretical Computer Science
- Computer Science(all)