An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement
AbstractBased on the advantages of a non-subsampled shearlet transform (NSST) in image processing and the characteristics of remote sensing imagery, NSST was applied to enhance blurred images. In the NSST transform domain, directional information measurement can highlight textural features of an image edge and reduce image noise. Therefore, NSST was applied to the detailed enhancement of high-frequency sub-band coefficients. Based on the characteristics of a low-frequency image, the retinex method was used to enhance low-frequency images. Then, an NSST inverse transformation was performed on the enhanced low- and high-frequency coefficients to obtain an enhanced image. Computer simulation experiments showed that when compared with a traditional image enhancement strategy, the method proposed in this paper can enrich the details of the image and enhance the visual effect of the image. Compared with other algorithms listed in this paper, the brightness, contrast, edge strength, and information entropy of the enhanced image by this method are improved. In addition, in the experiment of noisy images, various objective evaluation indices show that the method in this paper enhances the image with the least noise information, which further indicates that the method can suppress noise while improving the image quality, and has a certain level of effectiveness and practicability. View Full-Text
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Qu, Z.; Xing, Y.; Song, Y. An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement. Information 2018, 9, 308.
Qu Z, Xing Y, Song Y. An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement. Information. 2018; 9(12):308.Chicago/Turabian Style
Qu, Zhi; Xing, Yaqiong; Song, Yafei. 2018. "An Image Enhancement Method Based on Non-Subsampled Shearlet Transform and Directional Information Measurement." Information 9, no. 12: 308.
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