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The algorithm presented in [24] is based on histogram modification.
To solve the overflow/underflow problem, after IWT, a method based on histogram modification is proposed.
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Other brightness preserving histogram-based enhancement techniques are based on histogram matching (HM) procedure.
Secondly, a low complexity auto white balance hardware architecture is presented based on histogram equalization algorithm.
Motivated by this, a novel automatic grayscale image colorization method based on histogram regression is presented in this paper.
We study several algorithms based on histogram differences and evaluate them with respect to precision, recall, and processing latency.
We constructed these categories based on histogram distributions of the XRF bromine readings.
The algorithm is based on the histogram modification, in the spatial domain, of the original image.
Most of these techniques are based on global histogram modifications or local contrast transformations and edge analysis [1, 18, 20 22], because of their straight forward and intuitive implementation qualities.
As an entire algorithm, reversible data hiding based on histogram-difference-shift is also reported.
Linearity of the regression models was judged based on histograms and scatter plots.
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