Digital image processing (2nd ed.)
Digital image processing (2nd ed.)
Constrained Restoration and the Recovery of Discontinuities
IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative methods for total variation denoising
SIAM Journal on Scientific Computing - Special issue on iterative methods in numerical linear algebra; selected papers from the Colorado conference
Dynamic histogram warping of image pairs for constant image brightness
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol.2)-Volume 2 - Volume 2
Histogram modification via partial differential equations
ICIP '95 Proceedings of the 1995 International Conference on Image Processing (Vol. 3)-Volume 3 - Volume 3
A Variational Approach to Remove Outliers and Impulse Noise
Journal of Mathematical Imaging and Vision
Journal of VLSI Signal Processing Systems
Journal of Mathematical Imaging and Vision
Journal of Mathematical Imaging and Vision
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Analysis of Half-Quadratic Minimization Methods for Signal and Image Recovery
SIAM Journal on Scientific Computing
A histogram modification framework and its application for image contrast enhancement
IEEE Transactions on Image Processing
Contrast enhancement using brightness preserving bi-histogram equalization
IEEE Transactions on Consumer Electronics
IEEE Transactions on Consumer Electronics
Brightness preserving histogram equalization with maximum entropy: a variational perspective
IEEE Transactions on Consumer Electronics
Contrast restoration of weather degraded images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Image Processing
Joint Exact Histogram Specification and Image Enhancement Through the Wavelet Transform
IEEE Transactions on Image Processing
Nonlinear image recovery with half-quadratic regularization
IEEE Transactions on Image Processing
Exact Histogram Specification for Digital Images Using a Variational Approach
Journal of Mathematical Imaging and Vision
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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.