Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional signal and image processing
Two-dimensional signal and image processing
Image selective smoothing and edge detection by nonlinear diffusion. II
SIAM Journal on Numerical Analysis
Nonlinear multiscale representations for image segmentation
Computer Vision and Image Understanding
Smoothing and edge detection by time-varying coupled nonlinear diffusion equations
Computer Vision and Image Understanding
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Hierarchical Bayesian Approach to Image Restoration: Applications to Astronomical Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Minimum entropy restoration of star field images
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors
IEEE Transactions on Information Theory
Behavioral analysis of anisotropic diffusion in image processing
IEEE Transactions on Image Processing
Image recovery using the anisotropic diffusion equation
IEEE Transactions on Image Processing
Anisotropic diffusion of multivalued images with applications to color filtering
IEEE Transactions on Image Processing
Blind image restoration by anisotropic regularization
IEEE Transactions on Image Processing
Markovian reconstruction using a GNC approach
IEEE Transactions on Image Processing
The ZπM algorithm: a method for interferometric image reconstruction in SAR/SAS
IEEE Transactions on Image Processing
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Images secured from an astronomical telescope usually suffer from blur and from interference that scientists refer to as ''noise''. Therefore, good image restoration technique has become an important tool in astronomical observation. In this paper, we propose a modified anisotropic diffusion scheme to tackle the problem of image restoration in astronomy, especially in the case of nebula images. In such images, a mass of stars may be extremely bright but also may be spread randomly in dark space, and the shape of the nebula may therefore appear obscure. To restore the original appearance of a nebula, noisy stars must be filtered out and the detailed structure of the nebula must be well enhanced. The classical Perona-Malik anisotropic diffusion model that only considers gradient information cannot filter out noisy stars from the nebula image. In this study, we propose a modified anisotropic diffusion model that incorporates both gradient and gray-level variance information to remove ''sparking'' stars of various sizes and brightness in a nebula image. Experimental results from a number of astronomical nebula images have shown that the proposed anisotropic diffusion scheme can effectively remove noisy stars and maintain the shape of nebula in this particular case.