Proceedings of 3rd IEEE International Conference on Image Processing
DOI: 10.1109/icip.1996.559619
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Dual domain interactive image restoration: basic algorithm

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Cited by 9 publications
(4 citation statements)
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“…This approach throws information away instead of restoring the blotch based upon existing data. Another method [15] fills in holes in a patterned image based upon a prototype of that pattern. This method is again insufficient to repair waterblotches, both because information would be thrown out and because old photographs do not necessarily have a regular pattern.…”
Section: Existing Workmentioning
confidence: 99%
“…This approach throws information away instead of restoring the blotch based upon existing data. Another method [15] fills in holes in a patterned image based upon a prototype of that pattern. This method is again insufficient to repair waterblotches, both because information would be thrown out and because old photographs do not necessarily have a regular pattern.…”
Section: Existing Workmentioning
confidence: 99%
“…A considerable amount of literature is also available where researchers have used projections onto convex sets to perform image restoration of this kind. Hirani and Totsuka [35] present a dual domain iterative method to restore the parts of an image. The method requires the users to provide a noise mask, and a corresponding mask (repair subimage) and a sample subimage.…”
Section: Previous Workmentioning
confidence: 99%
“…Many POCS methods have been proposed where the super-resolution problem is solved by iteratively projecting a given image onto two or more convex sets, each of which represents each of the introduced constraints on the reconstructed image (e.g., [ 14 – 18 ]). The constraints to be introduced vary depending on the available prior knowledge of images: The knowledge that can be employed by the POCS method includes the range of pixel values [ 15 , 19 ], the fidelity of the data [ 15 , 17 , 20 ], and nonnegativity [ 16 , 17 ]. In our study, we assume that both the measured frequency range and the outer boundary of a target in a given MR image are known, which means the POCS method can be applied for improving the spatial resolution of the given MR image by representing the knowledge with two convex sets in an image space.…”
Section: Introductionmentioning
confidence: 99%