Histogram equalization

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

Logarithmic transform coefficient histogram matching with spatial equalization

Abstract: In this paper we propose an image enhancement algorithm that is based on utilizing histogram data gathered from transform domain coefficients that will improve on the limitations of the histogram equalization method. Traditionally, classical histogram equalization has had some problems due to its inherent dynamic range expansion. Many images with data tightly clustered around certain intensity values can be over enhanced by standard histogram equalization, leading to artifacts and overall tonal change of the image. In the transform domain, one has control over subtle image properties such as low and high frequency content with their respective magnitudes and phases. However, due to the nature of many of these transforms, the coefficient’s histograms may be so tightly packed that distinguishing them from one another may be impossible. By placing the transform coefficients in the logarithmic transform domain, it is easy to see the difference between different quality levels of images based upon their logarithmic transform coefficient histograms. Our results demonstrate that combing the spatial method of histogram equalization with logarithmic transform domain coefficient histograms achieves a much more balanced enhancement, that out performs classical histogram equalization.

Contrast-dependent saturation adjustment for outdoor image enhancement

Abstract: Outdoor images captured in bad-weather conditions usually have poor intensity contrast and color saturation since the light arriving at the camera is severely scattered or attenuated. The task of improving image quality in poor conditions remains a challenge. Existing methods of image quality improvement are usually effective for a small group of images but often fail to produce satisfactory results for a broader variety of images. In this paper, we propose an image enhancement method, which makes it applicable to enhance outdoor images by using content-adaptive contrast improvement as well as contrast-dependent saturation adjustment. The main contribution of this work is twofold: (1) we propose the content-adaptive histogram equalization based on the human visual system to improve the intensity contrast; and (2) we introduce a simple yet effective prior for adjusting the color saturation depending on the intensity contrast. The proposed method is tested with different kinds of images, compared with eight state-of-the-art methods: four enhancement methods and four haze removal methods. Experimental results show the proposed method can more effectively improve the visibility and preserve the naturalness of the images, as opposed to the compared methods.

Reliable histogram features for detecting LSB matching

Abstract: This paper proposes a novel steganalyzer for detecting one of the most popular steganography, LSB matching (also known as “±1 embedding”). The histogram of difference image (the differences of adjacent pixels), which is usually a generalized Gaussian distribution centered at 0, is exploited for deriving statistical features. We have proved theoretically that the peak-value of the histogram would decrease after LSB matching embedding, while the renormalized histogram (the ratio of the histogram to the peak-value) would increase. Then we take the peak-value and the renormalized histogram as features for classification. Extensive experimental results show that the proposed steganalytic method outperforms some previous ones.