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 published on a yearly basis
Papers
More filters
07 Dec 2015
TL;DR: The results obtained and the comparison with existing algorithms, both are sufficient enough to prove that the proposed algorithm is robust and effective.
Abstract: Target detection in synthetic aperture radar (SAR) images which are affected by speckle noise is a challenging task. An algorithm for automatic target detection in SAR images is proposed in this research work. In the first step, moving and stationary target acquisition and recognition (MSTAR) images are segmented and passed through multiple preprocessing stages (histogram equalization, dilation, position normalization). In the next step, feature extraction based on SIFT is performed. The extracted features from testing images are matched with the features extracted from training images. Thus, the classification of the targets is performed. The results obtained and the comparison with existing algorithms, both are sufficient enough to prove that the proposed algorithm is robust and effective.
26 citations
TL;DR: Experimental results show that the secret data can be embedded and extracted successfully without introducing visual artifacts, and the proposed scheme can always achieve a high capacity or maintain good image quality, comparing with the related works.
Abstract: This paper proposes a novel multibit assignment steganographic scheme for palette images, in which some colors in the palette are exploited to represent several secret bits. For the proposed scheme, each palette color is treated as a graph vertex, and an edge among any two vertices indicates an adjacent relationship between them. A graph traversal technique named depth-first search is used to accomplish the multibit assignment for the vertices that correspond to the palette colors. The major idea of the proposed data-embedding is to modify colors of image pixels according to the assigned bits and secret message. Image pixels are classified as embeddable pixels and non-embeddable pixels before data-embedding. During the data-embedding, for each embeddable pixel, if the original color of the pixel matches the secret data, the pixel is then kept unchanged; otherwise, a suitable adjacent color of the original color will be used to replace the original color so that the new color matches the secret data. Experimental results show that the secret data can be embedded and extracted successfully without introducing visual artifacts, and the proposed scheme can always achieve a high capacity or maintain good image quality, comparing with the related works.
26 citations
16 May 2014
TL;DR: A fog level detection method based on image HSV color histogram that can make qualitative judgments on foggy days quickly and the qualitative detection results are relatively good is proposed.
Abstract: As a direction in the development of computer vision, fog visibility detection is very important for traffic safety. Aiming at the visibility detection problem appearing in the highspeed road traffic, this paper proposes a fog level detection method based on image HSV color histogram. First, convert the background image color space from RGB color space to HSV color space. And then achieve the fog level detection by classifying fog weathers of different visibility level into different types using the image HSV color histogram features (including H, S, and V) in various weather conditions. The experimental results show that this method can make qualitative judgments on foggy days quickly and the qualitative detection results are relatively good.
26 citations
TL;DR: The results show that, compared with the equalized image of each RGB color channel using the traditional method, the proposed method yields superior results, with higher accuracy in terms of mean squared error and peak signal-to-noise ratio, when applied to retinal and prostate cancer images.
Abstract: Image enhancement is crucial in medical imaging. Histogram equalization is an image enhancement technique employed to enhance image contrast, which has become a vital part of general and medical image processing. Although it is widely studied and applied, traditional histogram equalization achieves poor image enhancement results because it does not consider hue preservation. This study proposes a novel image enhancement method that incorporates hue preservation to address this problem. The results show that, compared with the equalized image of each RGB color channel using the traditional method, the proposed method yields superior results, with higher accuracy in terms of mean squared error and peak signal-to-noise ratio, when applied to retinal and prostate cancer images. This can effectively assist physicians in making the proper judgment.
26 citations
TL;DR: This paper surveys some of HE based methods, namely Mean Brightness Preserving HE (MBPHE), Bin Modified HE (BMHE), and Local HE (LHE), which generally fall into three main families of HE.
Abstract: —Global Histogram Equalization (GHE) is a well-known image enhancement method. Despite of its simplicity and popularity, GHE still has limitations. GHE usually causes the shifting of the mean luminance of the image, produces artifacts and unnatural enhancements, and does not consider local information in its process. Therefore, these limitations lead to the development of several histogram equalization (HE) methods. This paper surveys some of HE based methods. These methods generally fall into three main families of HE, namely Mean Brightness Preserving HE (MBPHE), Bin Modified HE (BMHE), and Local HE (LHE).
26 citations