A fuzzy fusion approach for modified contrast enhancement based image forensics against attacks

In today’s digital age the trustworthy towards image is distorting because of malicious forgery images. The issues related to the multimedia security have led to the research focus towards tampering detection. The main objective of the work is to develop robust and forensic detection framework against post processing. It is also essential to enhance the security against attacks. In this paper, a Modified Contrast Enhancement based Forensics (MCEF) method based on Fuzzy Fusion is proposed against post-processing activity. First, we check for the histogram peaks and gaps as a result of contrast enhancement which is used in the latest technique. From the standpoint of attackers, we use two types of attacks, CE trace hiding attack and CE trace forging attack, which could invalidate the forensic detector and fabricate two types of forensic errors, consequently. The CE trace hiding attack is implemented by integrating local random dithering into the form of pixel value mapping. The CE trace forging attack is proposed by modifying the grey level histogram of a target pixel region to fraudulent peak/gap artifacts. Then both attacks are added to enhanced images as a post processing activity. As a result the gaps get disappeared, but introduced sudden peaks. Then, feature selection methods in conjunction with fuzzy fusion approach is suggested to enhance the robustness of tamper detection methods. The threshold value for contrast detection is increased, so we can identify the contrast enhancement. The Artificial Neural Network (ANN) is used instead of SVM, it increases the robustness and accuracy of the digital images. The proposed methodology will be implemented using MATLAB and validated by comparing with the conventional techniques.     

Two-dimensional histogram equalization and contrast enhancement

In this paper, we propose a two-dimensional histogram equalization (2DHE) algorithm which utilizes contextual information around each pixel to enhance the contrast of an input image. The algorithm is based on the observation that the contrast in an image can be improved by increasing the grey-level differences between each pixel and its neighbouring pixels. The image equalization is achieved by assuming that for a given image, the modulus of the grey-level differences between pixels and their neighbouring pixels are equally distributed. The well-known global histogram equalization algorithm is a special case of 2DHE when contextual information is not utilized. 2DHE is easy to implement requiring only a small number of simple arithmetic operations and is thus suitable for real-time contrast enhancement applications. Experimental results show that 2DHE produces better or comparable enhanced images than several state-of-the-art algorithms. The only parameter in 2DHE which requires tuning is the size of the spatial neighbourhood support which provides the contextual information for a given dynamic range of the enhanced image. An automated parameter selection algorithm is also presented. The algorithm can be applied to a wide range of image types.     

Multi scale entropy based adaptive fuzzy contrast image enhancement for crowd images

Multimedia Tools and Applications - Contrast enhancement is a very important issue in image processing, pattern recognition and computer vision. Fuzzy logic based techniques perform enhancement...     

最小模糊偏移度准则下的SAR图像对比度增强

针对SAR图像灰度特性,提出最小模糊偏移自动对比度增强算法(MFO)。该算法基于模糊理论,利用高斯型隶属度函数将图像灰度信息模糊化,以模糊偏移度最小准则确定模糊对比度增强操作数(INT),得到该准则下最优的S形灰度映射函数,增强SAR图像对比度。利用TEN、EME两种评估参数评价增强结果,验证了算法的有效性。     

A novel scheme for infrared image enhancement by using weighted least squares filter and fuzzy plateau histogram equalization

High-quality thermal infrared (IR) images are always preferred in numerous real-world applications. However, acquired IR images, which have low contrast and signal-to-noise ratio (SNR) among other characteristics, have inferior quality because of various factors. To improve the quality of IR images, three main aspects must be addressed: global contrast, local contrast, and noise of IR images. Most of the existing methods focus only on some of these issues. In this paper, we propose a novel scheme to solve the three issues. First, an edge-preserving filter called weighted least squares filter is adopted to decompose an IR image into a low-frequency (LF) component and a sequence of high-frequency (HF) components. We propose a fuzzy plateau histogram equalization for the LF component to improve global contrast. A strategy is exploited to alter the coefficients of the HF components to enhance local contrast. The primitive result is synthesized with the enhanced LF and HF components. To suppress the noise in the primitive result, nonlocal means filter is applied to derive the final result. Numerous experiments are conducted. Experimental results demonstrate that the proposed scheme exhibits the best performance compared with the other methods.     

A new fuzzy logic filter for image enhancement

This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.     

Image contrast enhancement for intelligent surveillance systems using multi-local histogram transformation

Among all applications to monitor the safety and security of working environments, surveillance systems that use computer vision are the most efficient and intuitive in the manufacturing industry. This paper introduces a new technique of contrast enhancement for surveillance systems using computer vision. The histogram equalization method is a common and widespread image enhancement method which maximizes the contrast of the image. This contrast enhancement method usually improves the quality of images, but it can suffer from visual deterioration caused by excessive histogram modification. To overcome the limitations of conventional contrast enhancement methods, this paper introduces a new multi-local histogram transformation method for surveillance systems. This technique is based on the local histograms, which are separated from the overall histogram of the image, and the contrast of the image can be enhanced through two major processes: range reassignment of local histograms and local histogram equalization. The multi-local histogram transformation in this paper enhances the contrast of images, preventing excessive compression and extension of image histograms. The performance of the suggested contrast enhancement method is verified by the experiments in four different environments.     

A spatially variant white-patch and gray-world method for color image enhancement driven by local contrast

Starting from the revolutionary Retinex by Land and McCann, several further perceptually inspired color correction models have been developed with different aims, e.g. reproduction of color sensation, robust features recognition, enhancement of color images. Such models have a differential, spatially-variant and non-linear nature and they can coarsely be distinguished between white-patch (WP) and gray-world (GW) algorithms. In this paper we show that the combination of a pure WP algorithm (RSR: Random Spray Retinex) and an essentially GW one (ACE) leads to a more robust and better performing model (RACE). The choice of RSR and ACE follows from the recent identification of a unified spatially-variant approach for both algorithms. Mathematically, the originally distinct non-linear and differential mechanisms of RSR and ACE have been fused using the spray technique and local average operations. The investigation of RACE allowed us to put in evidence a common drawback of differential models: corruption of uniform image areas. To overcome this intrinsic defect, we devised a local and global contrast-based and image-driven regulation mechanism that has a general applicability to perceptually inspired color correction algorithms. Tests, comparisons and discussions are presented.     

Image contrast enhancement using unsharp masking and histogram equalization

Multimedia Tools and Applications - Contrast enhancement and Mean brightness conservation are two important parameters of image enhancement. A high contrast image is good in subjective quality...     

Robust perceptual image hashing using fuzzy color histogram

Multimedia Tools and Applications - Perceptual image hashing technique uses the appearance of the digital media object as human eye and generates a fixed size hash value. This hash value works as...     

基于粗糙集和属性直方图的医学图像增强

医学图像信息存在着复杂性,在处理中的各个不同层次可能出现不完整性和不确定性。利用粗糙集理论进行图像增强,子图的划分是关键。属性直方图是对直方图概念的推广,是一种由先验知识约束的直方图,将它用于子图划分。在此基础上本文提出了一种基于粗糙集和属性直方图的医学图像增强方法。利用属性直方图的Otsu算法确定灰度阈值,根据灰度阈值利用不可分辨关系,将图像划分为背景子图、目标子图和噪声子图,对噪声点用中值滤波进行滤除,去噪后的背景子图和目标子图进行增强变换,合并得到增强图像。以胸部CT图像中的肺组织为目标区域,进行大量的实验,结果表明该方法明显增强了图像且不损害图像的边缘。     

A method of video image enhancement

A new method of image enhancement that allows improving the quality of required image details is proposed. To enhance local contrasts, a special transformation function is submitted. By the example of enhancement of a video-shot image of a car, we show the efficiency of the proposed technique in comparison with known methods of contrast enhancement.     

Directed searching optimized mean-exposure based sub-image histogram equalization for grayscale image enhancement

Multimedia Tools and Applications - This paper presents a novel enhancement technique for low light grayscale images. The main goal of this work is to enhance the visual quality and improve the...     

基于小波域的图像自适应模糊增强

本文结合小波变换和模糊逻辑提出一种新的图像增强方法,利用改进的自适应模糊权重中值滤波滤除噪声,应用模糊增强算子对图像增强.经试验仿真,该方法在去噪的的过程中保留了大部分细节及边缘信息,同时改善了视觉效果.     

A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting

Recently, resolving the problem of evaluation and ranking the potential suppliers has become as a key strategic factor for business firms. With the development of intelligent and automated information systems in the information era, the need for more efficient decision making methods is growing. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable criteria assuming that compromising is acceptable to resolve conflicts. On the other side objective weights based on Shannon entropy concept could be used to regulate subjective weights assigned by decision makers or even taking into account the end-users’ opinions. In this paper, we treat supplier selection as a group multiple criteria decision making (GMCDM) problem and obtain decision makers’ opinions in the form of linguistic terms. Then, these linguistic terms are converted to trapezoidal fuzzy numbers. We extended the VIKOR method with a mechanism to extract and deploy objective weights based on Shannon entropy concept. The final result is obtained through next steps based on factors R, S and Q. A numerical example is proposed to illustrate an application of the proposed method.     

Joint optimization and perceptual boosting of global and local contrast for efficient contrast enhancement

Multimedia Tools and Applications - Contrast enhancement which aims to increase the contrast of an image with low dynamic range, has been widely studied and exploited. In spite of the great success...     

Low light enhancement algorithm for color images using intuitionistic fuzzy sets with histogram equalization

Multimedia Tools and Applications - In this work, a new fuzzy logic-based algorithm is proposed for the enhancement of low light color images. A generalization of a fuzzy set known as an...