Topic
Histogram equalization
About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.
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TL;DR: This paper presents a new color-conversion method that offers users an intuitive, one-click interface for style conversion, rather than having to supply a reference image, and achieves more accurate and justifiable color conversion results, while also preserving spatial coherence.
Abstract: This paper presents a new color-conversion method that offers users an intuitive, one-click interface for style conversion. So, rather than having to supply a reference image, users simply select a color mood with a mouse click. To address the color-transfer quality problem, we associate each color mood with a set of up to 10 images from our image database. After selecting their color mood, users choose one associated image. Our histogram matching algorithm then uses the selected image to determine the input image's color distribution. We thereby achieve more accurate and justifiable color conversion results, while also preserving spatial coherence. Here, we further describe our solutions and their results and compare them to existing approaches.
64 citations
09 May 2005
TL;DR: A refinement of histogram equalization which uses both global and local information to remap the image grey levels and finds that its methods can provide an improvement in contrast enhancement versus HE, while avoiding undesirable over-enhancement that can occur with LHE and other methods.
Abstract: We present a refinement of histogram equalization which uses both global and local information to remap the image grey levels. Local image properties, which we generally call neighborhood metrics, are used to subdivide histogram bins that would be otherwise indivisible using classical histogram equalization (HE). Choice of the metric influences how the bins are subdivided, affording the opportunity for additional contrast enhancement. We present experimental results for two specific neighborhood metrics and compare the results to classical histogram equalization and local histogram equalization (LHE). We find that our methods can provide an improvement in contrast enhancement versus HE, while avoiding undesirable over-enhancement that can occur with LHE and other methods. Moreover, the improvement over HE is achieved with only a small increase in computation time.
64 citations
TL;DR: In this paper, an efficient color space for contrast enhancement of myocardial perfusion images was established, where contrast limited adaptive histogram equalization was applied to the chrominance channels of the cardiac nuclear image, leaving the luminance channel unaffected.
64 citations
TL;DR: The hypothesis that the quality of the image, which is enhanced at the pre-processing stage, can play a significant role in enhancing the classification performance of any statistical approach is presented.
64 citations
TL;DR: The proposed approach is found to be highly appreciable for overall enhancement, preserving all the intrinsic visual details for a wide range of dark image database covering satellite as well as general images.
63 citations