A Sparse Representation Image Denoising Method Based on Orthogonal Matching Pursuit

Image denoising is an important research aspect in the field of digital image processing, and sparse representation theory is also one of the research focuses in recent years. The sparse representation of the image can better extract the nature of the image, and use a way as concise as possible to express the image. In image denoising based on sparse representation, the useful information of the image possess certain structural features, which match the atom structure. However, noise does not possess such property, therefore, sparse representation can effectively separate the useful information from noise to achieve the purpose of denoising. Aiming at image denoising problem of low signal-to-noise ratio (SNR) image, combined with Orthogonal Matching Pursuit and sparse representation theory, this paper puts forward an image denoising method. The experiment shows that compared with the traditional image denoising based on Symlets, image denoising based on Contourlet transform, this method can delete noise in low SNR image and keep the useful information in the original image more efficiently

Applications of Improved Ant Colony Optimization Clustering Algorithm in Image Segmentation

When expressing the data feature extraction of the interesting objectives, image segmentation is to transform the data set of the features of the original image into more tight and general data set. This paper explores the image segmentation technology based on ant colony optimization clustering algorithm and proposes an improved ant colony clustering algorithm (ACCA). It improves and analyzes the computational formula of the similarity function and improves parameter selection and setting by setting ant clustering rules. Through this algorithm, it can not only accelerate the clustering speed, but it can also have a better clustering partitioning result. The experimental result shows that the method of this paper is better than the original OTSU image segmentation method in accuracy, rapidity and stability

RVM Classification of Hyperspectral Images Based on Wavelet Kernel Non-negative Matrix Fractorization

A novel kernel framework for hyperspectral image classification based on relevance vector machine (RVM) is presented in this paper. The new feature extraction algorithm based on Mexican hat wavelet kernel non-negative matrix factorization (WKNMF) for hyperspectral remote sensing images is proposed. By using the feature of multi-resolution analysis, the new method of nonlinear mapping capability based on kernel NMF can be improved. The new classification framework of hyperspectral image data combined with the novel WKNMF and RVM. The simulation experimental results on HYDICE and AVIRIS data sets are both show that the classification accuracy of proposed method compared with other experiment methods even can be improved over 10% in some cases and the classification precision of small sample data area can be improved effectively

Orbital Origin of Extremely Anisotropic Superconducting Gap in Nematic Phase of FeSe Superconductor

The iron-based superconductors are characterized by multiple-orbital physics where all the five Fe 3$d$ orbitals get involved. The multiple-orbital nature gives rise to various novel phenomena like orbital-selective Mott transition, nematicity and orbital fluctuation that provide a new route for realizing superconductivity. The complexity of multiple-orbital also asks to disentangle the relationship between orbital, spin and nematicity, and to identify dominant orbital ingredients that dictate superconductivity. The bulk FeSe superconductor provides an ideal platform to address these issues because of its simple crystal structure and unique coexistence of superconductivity and nematicity. However, the orbital nature of the low energy electronic excitations and its relation to the superconducting gap remain controversial. Here we report direct observation of highly anisotropic Fermi surface and extremely anisotropic superconducting gap in the nematic state of FeSe superconductor by high resolution laser-based angle-resolved photoemission measurements. We find that the low energy excitations of the entire hole pocket at the Brillouin zone center are dominated by the single $d_{xz}$ orbital. The superconducting gap exhibits an anti-correlation relation with the $d_{xz}$ spectral weight near the Fermi level, i.e., the gap size minimum (maximum) corresponds to the maximum (minimum) of the $d_{xz}$ spectral weight along the Fermi surface. These observations provide new insights in understanding the orbital origin of the extremely anisotropic superconducting gap in FeSe superconductor and the relation between nematicity and superconductivity in the iron-based superconductors.Comment: 19 pages, 4 figure