Histogram equalization

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

Blind forensics of contrast enhancement in digital images

Abstract: Digital images have seen increased use in applications where their authenticity is of prime importance. This proves to be problematic due to the widespread availability of digital image editing software. As a result, there is a need for the development of reliable techniques for verifying an image's authenticity. In this paper, a blind forensic algorithm is proposed for detecting the use of global contrast enhancement operations to modify digital images. Furthermore, a separate algorithm is proposed to identify the use of histogram equalization, a commonly implemented contrast enhancement operation. Both algorithms perform detection by seeking out unique artifacts introduced into an image's histogram as a result of the particular operation examined. Additionally, results are presented showing the effectiveness of both proposed algorithms.

Enhancement of low quality underwater image through integrated global and local contrast correction

Abstract: A method of integration global and local contrast stretching is proposed to increase underwater image quality.The integrated modification of RGB and HSV color model is applied in the proposed method.Resultant image outperforms the current state-of-the-arts techniques in terms of image details and noise.300 low contrast underwater images are used in the experiment. The attenuation of the light that travels through a water medium subjects underwater images to several problems. As a result of low contrast and color performance, images are unclear and lose important information. Therefore, the objects in these images can hardly be differentiated from the background. This study proposes a new method called dual-image Rayleigh-stretched contrast-limited adaptive histogram specification, which integrates global and local contrast correction. The aims of the proposed method are to increase image details and to improve the visibility of underwater images while enhancing image contrasts. The two main steps of the proposed method are contrast and color corrections; an underwater image undergoes the former before the latter. Global contrast correction generates dual-intensity images, which are then integrated to produce contrast-enhanced resultant images. Subsequently, such images are processed locally to enhance details. The color of the images is also corrected to improve saturation and brightness. Qualitative and quantitative results show that the contrast of the resultant image improves significantly. Moreover, image detail and color are adequately enhanced; thus, the proposed approach outperforms current state-of-the-art methods.

Anatomy of a color histogram

Abstract: It is shown that the color histogram has an even closer relationship to scene properties than has been previously described. Color histograms have identifiable features that relate in a precise mathematical way to scene properties. Object color and illumination color are the most obvious properties that are related to color distribution, and their extraction has already been described. It is shown here that the histogram of color variation may be further exploited to relate its shape to surface roughness and imaging geometry. An understanding of these features allows an improved estimate of illumination color and object color to be made. >

Gradient Histogram Estimation and Preservation for Texture Enhanced Image Denoising

Abstract: Natural image statistics plays an important role in image denoising, and various natural image priors, including gradient-based, sparse representation-based, and nonlocal self-similarity-based ones, have been widely studied and exploited for noise removal. In spite of the great success of many denoising algorithms, they tend to smooth the fine scale image textures when removing noise, degrading the image visual quality. To address this problem, in this paper, we propose a texture enhanced image denoising method by enforcing the gradient histogram of the denoised image to be close to a reference gradient histogram of the original image. Given the reference gradient histogram, a novel gradient histogram preservation (GHP) algorithm is developed to enhance the texture structures while removing noise. Two region-based variants of GHP are proposed for the denoising of images consisting of regions with different textures. An algorithm is also developed to effectively estimate the reference gradient histogram from the noisy observation of the unknown image. Our experimental results demonstrate that the proposed GHP algorithm can well preserve the texture appearance in the denoised images, making them look more natural.

A blind DCT based color watermarking algorithm for embedding multiple watermarks

Abstract: Multiple watermarking based techniques are receiving more attention in recent times for its wide variety of applications in different fields. To protect the copyright ownership and validate the authenticity of multiple owners, in this paper a color multiple watermarking method based on DCT (Discrete Cosine Transform) and repetition code is proposed and simulated. Initially, green and blue components of color host image are selected for inserting multiple watermarks. Then, each green and blue component of the image is decomposed into non overlapping blocks and subsequently DCT is employed on each block. In this technique, a binary bit of watermark is embedded into green/blue component’s transformed block by modifying some middle significant AC coefficients using repetition code. During multiple watermarks embedding in green and blue components of the proposed method, DC and some higher AC coefficients are kept intact after zigzag scanning of each DCT block to ensure the imperceptibility of the watermarked host image. The proposed scheme is experimented to establish the validity by extracting adequate multiple watermark data from the restructured cover image after applying common geometric transformation attacks (like rotation, cropping, scaling and deletion of lines/columns etc.), common enhancement technique attacks (like lowpass filtering, histogram equalization, sharpening, gamma correction, noise addition etc.) and JPEG compression attacks.