Based 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.
To exact the more directional information and important detail information from the images effectively, a novel image fusion algorithm for SAR and gray visible image based on the Hidden Markov Model in the Non-subsample Shearlet Transform (NSST) domain is proposed. In NSST domain, the low frequency coefficients are fused by standard deviation. Meanwhile, the NHMT model is built to train the high frequency coefficients. After that, the energy of gradient is used to select the trained coefficients. Then, the low frequency and high frequency images are fused by inverse transformation of NSST to get the final image. Finally, the simulation proves that compared with other mufti-scale HMT models and traditional NSST fusion strategy, the proposed method in this paper can promote the fusion quality and enhance the information of the images, reducing noise as well.
In this study, pulse coupled neural network (PCNN) was modified and applied to the enhancement of blur images. In the transform domain of nonsubsample shearlet transform (NSST), PCNN was used to enhance the details of images in the low- and high-frequency subbands, and then the enhanced low- and high-frequency coefficients were used for NSST inverse transformation to obtain the enhanced images. The results showed that the proposed method can produce higher-quality images and suppress noise better than traditional image enhancement strategies.
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