TY - GEN
T1 - A variational approach for exact histogram specification
AU - Chan, Raymond
AU - Nikolova, Mila
AU - Wen, You-Wei
PY - 2011/5
Y1 - 2011/5
N2 - 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.
AB - 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.
KW - convex minimization
KW - Exact histogram specification
KW - restoration from quantization noise
KW - smooth nonlinear optimization
KW - strict-ordering
KW - variational methods
UR - https://www.scopus.com/pages/publications/84855667822
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84855667822&origin=recordpage
U2 - 10.1007/978-3-642-24785-9_8
DO - 10.1007/978-3-642-24785-9_8
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 978-3-642-24784-2
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 86
EP - 97
BT - Scale Space and Variational Methods in Computer Vision - Third International Conference, SSVM 2011, Revised Selected Papers
A2 - Bruckstein, Alfred M.
A2 - Romeny, Bart M. ter Haar
A2 - Bronstein, Alexander M.
A2 - Bronstein, Michael M.
PB - Springer Berlin Heidelberg
T2 - 3rd International Conference on Scale Space and Variational Methods in Computer Vision, SSVM 2011
Y2 - 29 May 2011 through 2 June 2011
ER -