In order to adaptively adjust the model parameters and the global threshold for image segmentation, an improved pulse coupled neural network (PCNN) model based on human visual system (HVS) is proposed in this paper. Due to the property of HVS that human visual sensitivity to an image varies with different regions of the image where different regions correspond to different information rate area of the image, we analyze the characteristics of the improved model and its parameter optimization principle,and propose an improved segmentation algorithm. According to the gray scale of pixels, the algorithm adaptively realizes the division of the image information area. It not only preserves the excellent characteristics of PCNN for image segmentation, but also effectively preserves the gradation of image itself. The experiment results show that the algorithm proposed is efficient and has good segmentation effect, and has a wide application prospect in remote sensing image processing.
KEYWORDS: Image quality, Image compression, Image processing, Remote sensing, Principal component analysis, Algorithm development, Signal to noise ratio
Remote sensing image quality assessment and improvement is an important part of image processing. Generally, the use of compressive sampling theory in remote sensing imaging system can compress images while sampling,which can improve efficiency. A method of two-dimensional principal component analysis(2DPCA) is proposed to reconstruct the remote sensing image to improve the quality of the compressed image in this paper, which contain the useful information of image and can restrain the noise. Then, remote sensing image quality influence factors are analyzed, and the evaluation parameters for quantitative evaluation are introduced. On this basis, the quality of the reconstructed images is evaluated and the different factors influence on the reconstruction is analyzed, providing meaningful referential data for enhancing the quality of remote sensing images. The experiment results show that evaluation results fit human visual feature, and the method proposed have good application value in the field of remote sensing image processing.
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