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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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Journal Article
TL;DR: A combination of four feature extraction methods namely color Histogram, Color Moment, texture, and Edge Histogram Descriptor is used for retrieval of images and the averages of the four techniques are made and the resultant Image is retrieved.
Abstract: There are numbers of methods prevailing for Image Mining Techniques This Paper includes the features of four techniques I,e Color Histogram, Color moment, Texture, and Edge Histogram Descriptor The nature of the Image is basically based on the Human Perception of the Image The Machine interpretation of the Image is based on the Contours and surfaces of the Images The study of the Image Mining is a very challenging task because it involves the Pattern Recognition which is a very important tool for the Machine Vision system A combination of four feature extraction methods namely color Histogram, Color Moment, texture, and Edge Histogram Descriptor There is a provision to add new features in future for better retrieval efficiency In this paper the combination of the four techniques are used and the Euclidian distances are calculated of the every features are added and the averages are made The user interface is provided by the Mat lab The image properties analyzed in this work are by using computer vision and image processing algorithms For color the histogram of images are computed, for texture co occurrence matrix based entropy, energy, etc, are calculated and for edge density it is Edge Histogram Descriptor (EHD) that is found For retrieval of images, the averages of the four techniques are made and the resultant Image is retrieved Keywords-component; Content Based Image Retrieval (CBIR), Edge Histogram Descriptor (EHD),Color moment ,textures, Color Histogram

50 citations

Journal ArticleDOI
TL;DR: Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.
Abstract: Image mosaicking applications require both geometrical and photometrical registrations between the images that compose the mosaic. This paper proposes a probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image. An extensive comparison with ten other state of the art color correction algorithms is presented, using two different image pair data sets. Results show that the proposed approach obtains the best average scores in both data sets and evaluation metrics and is also the most robust to failures.

50 citations

Journal ArticleDOI
TL;DR: The non-linear gamma correction method is adopted to enhance the contrast, while a weighted sum approach is employed for brightness preservation, and results have shown that the proposed method outperforms currently available methods in contrast to enhancement and brightness preservation.
Abstract: The enhancement of image contrast and preservation of image brightness are two important but conflicting objectives in image restoration. Previous attempts based on linear histogram equalization had achieved contrast enhancement, but exact preservation of brightness was not accomplished. A new perspective is taken here to provide balanced performance of contrast enhancement and brightness preservation simultaneously by casting the quest of such solution to an optimization problem. Specifically, the non-linear gamma correction method is adopted to enhance the contrast, while a weighted sum approach is employed for brightness preservation. In addition, the efficient golden search algorithm is exploited to determine the required optimal parameters to produce the enhanced images. Experiments are conducted on natural colour images captured under various indoor, outdoor and illumination conditions. Results have shown that the proposed method outperforms currently available methods in contrast to enhancement and...

49 citations

Journal ArticleDOI
TL;DR: Qualitatively, the ERMHE produces enhanced images with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation, and achieves the highest peak signal-to-noise-ratio (PSNR), lowest Absolute Mean Brightness Error (AMBE), and second best in Discrete Entropy (DE) scores.
Abstract: Non-uniform illuminated images pose challenges in contrast enhancement due to the existence of different exposure region caused by uneven illumination. Although Histogram Equalization (HE) is a well-known method for contrast improvement, however, the existing HE-based enhancement methods for non-illumination often generated the unnatural images, introduced unwanted artifacts, and washed out effect because they do not utilize the information from the different exposure regions in performing equalization. Therefore, this study proposes a modified HE-based contrast enhancement technique for non-uniform illuminated images namely Exposure Region-Based Multi-Histogram Equalization (ERMHE). The ERMHE uses exposure region-based histogram segmentation thresholds to segment the original histogram into sub-histograms. With the thresholded sub-histograms, the ERMHE then uses an entropy-controlled gray level allocation scheme to allocate new output gray level range and to obtain new thresholds that will be used to repartition the histogram prior to HE process. A total of 154 non-uniform illuminated sample images are used to evaluate the application of the proposed ERMHE. By comparing ERMHE to four existing HE-based contrast enhancement namely, Global HE, Mean Preserving Bi-Histogram Equalization (BBHE), Dualistic Sub-Image Histogram Equalization (DSIHE), and Contrast Limited Adaptive Histogram Equalization (CLAHE), qualitatively, the ERMHE produces enhanced images with a natural appearance, appealing contrast, less degradation, and reasonable detail preservation. Quantitatively, the ERMHE achieves the highest peak signal-to-noise-ratio (PSNR), lowest Absolute Mean Brightness Error (AMBE), and second best in Discrete Entropy (DE) scores. From the analyses, the ERMHE has shown its capability in enhancing different exposure regions exist in non-uniform illuminated images.

49 citations

Proceedings ArticleDOI
01 Aug 2017
TL;DR: An improved image enhancement on digital chest radiography using the so-called N-CLAHE method, which is based on global and local enhancement, which yields great improvement on the pre-processing correction for digitalchest radiography.
Abstract: Digital chest radiography offers many advantages over filmbased radiography, such as immediate image display, no film processing and room storage, wider dynamic range and lower radiation dose. In general, a raw X-ray image acquired directly from a digital flat detector contains poor quality of image, which may not be suitable for diagnosis and treatment planning. Therefore, a pre-processing technique is usually required to enhance image quality. This paper presents an improved image enhancement on digital chest radiography using the so-called N-CLAHE method, which is based on global and local enhancement. The proposed technique consists of two main steps. Firstly, intensity correction of the raw image is encountered by the log-normalization function which adjusts the intensity contrast of the image dynamically. Secondly, the Contrast Limited Adaptive Histogram Equalization (CLAHE) method is used for enhancing small details, textures and local contrast of the images. The proposed approach was tested using a radiographic survey phantom and a radiographic chest phantom and compared with conventional enhancement methods, such as histogram equalization, unsharp masking, CLAHE. The results show that the proposed N-CLAHE method yields great improvement on the pre-processing correction for digital chest radiography.

49 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
2023115
2022280
2021186
2020248
2019267
2018267