A Variational Approach for Exact Histogram Specification
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員

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摘要We focus on exact histogram specification when the input image is quantified. The goal is to transform this input image into an output image whose histogram is exactly the same as a prescribed one. In order to match the prescribed histogram, pixels with the same intensity level in the input image will have to be assigned to different intensity levels in the output image. An approach to classify pixels with the same intensity value is to construct a strict ordering on all pixel values by using auxiliary attributes. Local average intensities and wavelet coefficients have been used by the past as the second attribute. However, these methods cannot enable strict-ordering without degrading the image. in this paper, we propose a variational approach to establish an image preserving strict-ordering of the pixel values. We show that strict-ordering is achieved with probability one. Our method is image preserving in the sense that it reduces the quantization noise in the input quantified image. Numerical results show that our method gives better quality images than the preexisting methods.
著者Chan R, Nikolova M, Wen YW
會議名稱3rd International Conference on Scale Space and Variational Methods in Computer Vision
會議開始日29.05.2011
會議完結日02.06.2011
會議地點Ein Gedi
會議國家/地區以色列
期刊名稱Lecture Notes in Artificial Intelligence
詳細描述To ORKTS: www.springer.com/computer/image+processing/book/978-3-642-24784-2
出版年份2012
月份1
日期1
卷號6667
出版社SPRINGER-VERLAG BERLIN
頁次86 - 97
國際標準書號978-3-642-24784-2
國際標準期刊號0302-9743
語言英式英語
關鍵詞convex minimization; Exact histogram specification; restoration from quantization noise; smooth nonlinear optimization; strict-ordering; variational methods
Web of Science 學科類別Computer Science; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

上次更新時間 2020-18-10 於 00:42