scispace - formally typeset
Search or ask a question
Topic

Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: The main objective of the proposed FRVM classification is to accurately predict the type of leaf from the given input leaf images, which showed better results such as accuracy, sensitivity and specificity of 99.87%, 99.5%, and 99.9% respectively, which are the improved values over the literature.

65 citations

Journal ArticleDOI
TL;DR: Results showed that the proposed colour image enhancement technique introduced in this work is able to recover the largest amount of information as compared to other current approaches, and provides satisfactory performances in terms of image contrast, and sharpness.
Abstract: The histogram equalization process is a simple yet efficient image contrast enhancement technique that generally produces satisfactory results. However, due to its design limitations, output images often experience a loss of fine details or contain unwanted viewing artefacts. One reason for such imperfection is a failure of some techniques to fully utilize the allowable intensity range in conveying the information captured from a scene. The proposed colour image enhancement technique introduced in this work aims at maximizing the information content within an image, whilst minimizing the presence of viewing artefacts and loss of details. This is achieved by weighting the input image and the interim equalized image recursively until the allowed intensity range is maximally covered. The proper weighting factor is optimally determined using the efficient golden section search algorithm. Experiments had been conducted on a large number of images captured under natural indoor and outdoor environment. Results s...

65 citations

Journal ArticleDOI
TL;DR: Experimental results show that the recognition ability of the system can be drastically enhanced after integrating these two image features, which are highly complementary to provide an image retrieval system.

65 citations

Proceedings ArticleDOI
22 Dec 2011
TL;DR: Comparison level of the images are quantified by the two proposed metrics, Histogram Flatness Measure and Histogram Spread, which reveal that HS is more meaningful than HFM.
Abstract: In this paper, contrast level of the images are quantified by the two proposed metrics. These metrics are Histogram Flatness Measure (HFM) and Histogram Spread (HS). Computation of these metrics is based on the shape of the histogram. Extensive simulation results reveal that HS is more meaningful than HFM. Low contrast images have low HS value, while high contrast images have higher value of HS. Thus HS metric can be used to distinguish between the images having different contrast level. Accuracy of the metric is also verified for natural and medical images. This metric has broad applications in image retrieval, image database management, visualization, rendering and image classification.

65 citations

Journal ArticleDOI
TL;DR: This study presents an up-to-date review over the application of NIOAs for HE variants in image enhancement domain and the main issues which are involved in the application.
Abstract: In the consumer electronics field, the main challenge in image processing is to preserve the original brightness. Histogram Equalization (HE) is one of the simplest and widely used methods for contrast enhancement. However, HE does not suit into the consumer electronics field as this procedure flattens the histogram by distributing the entire gray levels uniformly. Therefore, several HE variants have been proposed based on proper histogram segmentation, histogram weighting, and range optimization techniques to overcome this flattening effect. However, sometimes these modifications become complex and computationally expensive. Recently, researchers have formulated the HE variants for image enhancement as optimization problems and solved, using Nature-Inspired Optimization Algorithms (NIOA), which starts a new era in the image enhancement field. This study presents an up-to-date review over the application of NIOAs for HE variants in image enhancement domain. The main issues which are involved in the application of NIOAs with HE are also discussed here.

65 citations


Network Information
Related Topics (5)
Feature extraction
111.8K papers, 2.1M citations
87% related
Feature (computer vision)
128.2K papers, 1.7M citations
87% related
Image segmentation
79.6K papers, 1.8M citations
87% related
Image processing
229.9K papers, 3.5M citations
86% related
Convolutional neural network
74.7K papers, 2M citations
84% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023115
2022280
2021186
2020248
2019267
2018267