Shuaiqi Liu

Medical image fusion based on nuclear norm minimization

Medical image fusion plays an important role in diagnosis and treatment of diseases such as image‐guided radiotherapy and surgery. Although numerous medical image fusion methods have been proposed, most approaches have not touched the low rank nature of matrix formed by medical image, which usually lead to fusion image distortion and image information loss. These methods also often lack universality when dealing with different kinds of medical images. In this article, we propose a novel medical image fusion to overcome aforementioned issues on existing methods with the aid of low rank matrix approximation with nuclear norm minimization (NNM) constraint. The workflow of our method is described as: firstly, nonlocal similar patches across the medical image are searched by block matching for local patch in source images. Second, a fused matrix is stacking by shared nonlocal similarity patches, then the low rank matrix approximation methods under nuclear norm minimization can be used to recover low rank feature of fused matrix. Finally, fused image can be gotten by aggregating all the fused patches. Experimental results show that the proposed method is superior to other methods in both subjectively visual performance and objective criteria.

SAR Image Denoising via Sparse Representation in Shearlet Domain Based on Continuous Cycle Spinning

How to suppress speckle noise effectively has become one of the key problems in remote sensing image processing. This problem also restricts the development of key technology severely, especially in military applications and so on. To overcome the shortcoming that the optimal solution of image denoising based on sparse representation does not have one-to-one mapping of the original signal space, in this paper, we propose a novel synthetic aperture radar (SAR) image denoising via sparse representation in Shearlet domain based on continuous cycle spinning. First, the Shearlet transform is applied to the noised SAR image. Second, a new optimal denoising model is constructed using the sparse representation model based on the cycle spinning theory. Finally, the alternate iteration algorithm is used to solve the optimal denoising model to obtain the denoised image. The experimental results show that the proposed method not only effectively suppresses the speckle noise and improves the peak signal-to-noise ratio of denoising SAR image, but also obviously improves the visual effect of the SAR image, especially by enhancing the texture of the SAR image.

SAR image edge detection via sparse representation

In this paper, we propose a new synthetic aperture radar (SAR) image detection algorithm based on the de-noising algorithm via the sparse representation and a new morphology edge detector. Firstly, we apply the Shearlet transform to the SAR image to get the sparse representation of it. Then, morphological edge detector with direction is applied to directional sub-band coefficients of the Shearlet which are recovered by the iterative de-noising process. Finally, the completed SAR image edge is obtained by merging each sub-band edge using Dempster–Shafer evidence theory. By completely using the directional sub-bands of the Shearlet transform, the proposed algorithm overcomes the disadvantages of transform detection algorithms which are very unrobust to noise and can also generate inaccurate edges. The experimental results demonstrate the effectiveness and superiority of our proposed algorithm in terms of the edge positioning accuracy, integrity, and the number of false edge points.

Medical image fusion based on improved sum-modified-Laplacian

Sum‐modified‐Laplacian (SML) plays an important role in medical image fusion. However, fused rules based on larger SML always lead to fusion image distortion in transform domain image fusion or image information loss in spatial domain image fusion. Combined with average filter and median filter, a new medical image fusion method based on improved SML (ISML) is proposed. First, a basic fused image is gained by ISML, which is used for evaluation of the selection map of medical images. Second, difference images can be obtained by subtracting average image of all sources of medical images. Finally, basic fused image can be refined by difference images. The algorithm can both preserve the information of the source images well and suppress pixel distortion. Experimental results demonstrate that the proposed method outperforms the state‐of‐the‐art medical image fusion methods.

Speckle Suppression Based on Sparse Representation with Non-Local Priors

As speckle seriously restricts the applications of remote sensing images in many fields, the ability to efficiently and effectively suppress speckle in a coherent imaging system is indispensable. In order to overcome the over-smoothing problem caused by the speckle suppression algorithm based on classical sparse representation, we propose a non-local speckle suppression algorithm that combines the non-local prior knowledge of the image into the sparse representation. The proposed algorithm first applies shearlet to sparsely represent the input image. We then incorporate the non-local priors as constraints into the image sparse representation de-noising problem. The denoised image is obtained by utilizing an alternating minimization algorithm to solve the corresponding constrained de-noising problem. The experimental results show that the proposed algorithm can not only significantly remove speckle noise, but also improve the visual effect and retain the texture information of the image better.

DTI Image Denoising Based on Complex Shearlet Domain and Complex Diffusion Anisotropic Filtering

Diffusion tensor imaging (DTI) is an imaging modality that has developed in recent years. It is a non-invasive technique and needn’t contrast medium. However, the SNR of DTI data is relatively low and clinically polluted by noise, which can bring serious impacts on tensor calculating, fiber tracking and other post-processing. In order to reduce the influence of noise on DTI images and improve the efficiency of diffusion tensor imaging effectively, a new DTI denoising scheme is proposed by combining the complex Shearlet transform and complex diffusion anisotropic filtering. The experiment results acquired from the simulated and real data prove the good performance of the presented algorithm.

SAR Image De-Noising Based on Shift Invariant K-SVD and Guided Filter

Finding a way to effectively suppress speckle in SAR images has great significance. K-means singular value decomposition (K-SVD) has shown great potential in SAR image de-noising. However, the traditional K-SVD is sensitive to the position and phase of the characteristics in the image, and the de-noised image by K-SVD has lost some detailed information of the original image. In this paper, we present one new SAR image de-noising method based on shift invariant K-SVD and guided filter. The whole method consists of two steps. The first deals mainly with the noisy image with shift invariant K-SVD and obtaining the initial de-noised image. In the second step, we do the guided filtering for the initial de-noised image. Finally, we can recover the final de-noised image. Experimental results show that our method not only has better visual effects and objective evaluation, but can also save more detailed information such as image edge and texture when de-noising SAR images. The presented shift invariant K-SVD can be widely used in image processing, such as image fusion, edge detection and super-resolution reconstruction.