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In order to obtain higher resolution remote sensing images with more details, an improved sparse representation remote sensing image super-resolution reconstruction(SRR) algorithm is proposed. First, remote sensing image is preprocessed to obtain the required training sample image; then, the KSVD algorithm is used for dictionary training to obtain the high-low resolution dictionary pairs; finally, the image feature extraction block is represented, which is improved by using adaptive filtering method. At the same time, the mean value filtering method is used to improve the super-resolution reconstruction iterative calculation. Experiment results show that, compared with the most advanced sparse representation super-resolution algorithm, the improved sparse representation super-resolution method can effectively avoid the loss of edge information of SRR image and obtain a better super-resolution reconstruction effect. The texture details are more abundant in subjective vision, the PSNR is increased about 1 dB, and the structure similarity (SSIM) is increased about 0.01.

In order to obtain higher quality super-resolution reconstruction (SRR) of remote sensing images, an improved sparse representation remote sensing images SRR method is proposed in this paper. First, low-resolution image is processed by improved feature extract operator. The high-resolution image and low-resolution image blocks have the same sparse representation coefficient, so the SRR image with higher spatial resolution can be derived from the sparse representation coefficients which have been obtained from low-resolution image. The improved feature extraction operator is a method to get more detail and texture information from the training images. Experiment results show that more texture details can be obtained in the result of SRR remote sensing images subjectively. At the same time, the objective evaluation parameters are improved greatly. The peak PSNR is increased about 2.50dB and 0.50 dB, RMSE is decreased about 2.80 and 0.3 compared with bicubic interpolation algorithm and Ref[8] algorithm respectively.

Background: Breast cancer is the most common malignancy in women worldwide, so it is imperative to find new medications for this disease. The dry seeds of Brucea javanica have been extensively used in Traditional Chinese Medicine (TCM) for the treatment of tumor. Objectives: To isolate antitumor peptides from B. javanica seeds and research their antitumor activity with MCF-7 cells. Materials and Methods: Globulin of B. javanica was extracted and enzymatically hydrolyzed by pepsin. Then ultrafiltration, gel filtration chromatography (GFC), and reverse-phase high-performance liquid chromatography (RP-HPLC) were employed to purify the peptides. Anticancer activity was studied in humans in human breast cancer MCF-7 cells, MTT (3- [4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide)-assessed cell viability, Giemmsa staining determined whether the cells should undergo apoptosis, and apoptosis and cycle assays assessed the antitumor mechanism of the cells. Real time quantitative polymerase chain reaction (RT-qPCR) was used to study the alterations of oncogenes and cancer metastasis suppressor genes in MCF-7 cells treated with peptide extracts. Results: The fraction F9-9 extracted from B. javanica significantly inhibited the proliferation of MCF-7 cells with the IC50 of 0.124 ± 0.004 μg/mL. The possible mechanisms of this fraction were further explored. F9-9 obviously induced cell cycle arrest at G0/G1 phase and promoted apoptosis of MCF-7 cells. The underlying molecular mechanisms were explored by RT-qPCR and results showed that in B. javanica small molecular peptides upregulated the expression of tumor suppressor genes P53 and PTEN as well as tumor metastasis suppressor gene NM23H-1. Conclusion: This study suggests that B. javanica peptides have therapeutic activity and mechanisms for antibreast cancer, encouraging further isolation and purification to obtain monomeric peptides of this extract for anticancer use. [ABSTRACT FROM AUTHOR]