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: A computer-aided diagnosis (CAD) system based on the fractal analysis is proposed to classify the breast lesions into two classes: benign and malignant and the k-means classification method is used to classify benign tumors from malignant ones.
143 citations
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TL;DR: The proposed QDHE method outperforms some methods existing in literature by producing clearer enhanced images without any intensity saturation, noise amplification, and over-enhancement, and is suitable for images captured in low-light environments.
Abstract: In this paper, we introduce a histogram equalization (HE)-based technique, called quadrant dynamic histogram equalization (QDHE), for digital images captured from consumer electronic devices. Initially, the proposed QDHE algorithm separates the histogram into four (quadrant) sub-histograms based on the median of the input image. Then, the resultant sub-histograms are clipped according to the mean of intensity occurrence of input image before new dynamic range is assigned to each sub-histogram. Finally, each sub-histogram is equalized. Based on extensive simulation results, the QDHE method outperforms some methods existing in literature, which can be considered as state-of-the-arts, by producing clearer enhanced images without any intensity saturation, noise amplification, and over-enhancement. Furthermore, image details of the processed image are well preserved and highlighted. For this reason, the proposed QDHE algorithm is suitable for images captured in low-light environments - an unavoidable situation by many consumer electronics products such as camera devices in cell phone.
141 citations
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TL;DR: The proposed enhancement method has been shown to perform very well with insufficiently illuminated and noisy images, outperforming other conventional methods, in terms of contrast enhancement and noise reduction in the output image.
139 citations
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08 Nov 1999TL;DR: Three types of spatial color histograms are introduced: annular, angular and hybrid color histogram, and experiments show that, with a proper trade-off between the granularity in the color and spatial dimensions, these histograms outperform both the traditional color histographic and some existing histogram refinements such as the color coherent vector.
Abstract: The color histogram is an important technique for color image database indexing and retrieval. In this paper, the traditional color histogram is modified to capture the spatial layout information of each color, and three types of spatial color histograms are introduced: annular, angular and hybrid color histograms. Experiments show that, with a proper trade-off between the granularity in the color and spatial dimensions, these histograms outperform both the traditional color histogram and some existing histogram refinements such as the color coherent vector.
138 citations
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TL;DR: A constrained local histogram equalization method is proposed to balance the conflicting requirements: enhancement of the image details and the maintenance of the overall image appearance.
138 citations