Image quality assessment for contrast enhancement evaluation

Although the contrast enhancement (CE) is a great challenge, few efforts have been conducted on evaluation of the contrast changes. In this paper, we propose a contrast-changed image quality (CCIQ) metric including a local index, named edge-based contrast criterion (ECC), and three global measures. In the global measures, entropy, correlation coefficient and mean intensity are exploited. Particle swarm optimization (PSO) algorithm is utilized for obtaining an optimal combination of these quantities. Although the presented method utilizes the original image, it cannot be considered as a full-reference metric, since the original image is not regarded to have the ideal quality. Hence, it can be concluded that it follows a new paradigm in image quality assessment. Experimental results on the three benchmark databases, CID2013, TID2013 and TID2008 demonstrate that the proposed metric outperforms the-state-of-the-art methods.     

Enhancing underwater image via color correction and Bi-interval contrast enhancement

Underwater captured images often suffer from color cast and low visibility due to light is scattered and absorbed while it traveling in water. In this paper, we proposed a novel method of color correction and Bi-interval contrast enhancement to improve the quality of underwater images. Firstly, a simple and effective color correction method based on sub-interval linear transformation is employed to address color distortion. Then, a Gaussian low-pass filter is applied to the L channel to decompose the low- and high-frequency components. Finally, the low- and high-frequency components are enhanced by Bi-interval histogram based on optimal equalization threshold strategy and S-shaped function to enhancement image contrast and highlight image details. Inspired by the multi-scale fusion, we employed a simple linear fusion to integrate the enhanced high- and low-frequency components. Comparison with state-of-the-art methods show that the proposed method outputs high-quality underwater images with qualitative and quantitative evaluation well.     

Anti-forensics of median filtering and contrast enhancement

Digital images can be convincingly edited using image editing tools. In order to identify such image processing operations, various forensic techniques have been proposed. In response, anti-forensic operations designed as counter-measures have been devised. In this paper, we propose an anti-forensic technique to counter spatial domain forensic detectors and demonstrate its accuracy on popular image manipulation operations such as median filtering and contrast enhancement. The integrated anti-forensic attack is formulated as an optimization problem. The proposed optimization modifies the image so as to incorporate the median filtering or contrast enhancement operation while ensuring that its spatial characteristics do not change significantly. Through a series of experiments, we prove that the proposed algorithm can severely degrade the performance of median filtering and contrast enhancement detectors. The proposed algorithm also outperforms popular anti-forensic algorithms.     

A low light natural image statistical model for joint contrast enhancement and denoising

We study the problem of joint low light image contrast enhancement and denoising using a statistical approach. The low light natural image in the band pass domain is modeled by statistically relating a Gaussian scale mixture model for the pristine image, to the low light image, through a detail loss coefficient and Gaussian noise. The detail loss coefficient is statistically described using a posterior distribution with respect to its estimate based on a prior contrast enhancement algorithm. We then design our low light enhancement and denoising (LLEAD) method by computing the minimum mean squared error estimate of the pristine image band pass coefficients. We create the Indian Institute of Science low light image dataset of well-lit and low light image pairs to learn the model parameters and evaluate our enhancement method. We show through extensive experiments on multiple datasets that our method helps better enhance the contrast while simultaneously controlling the noise when compared to other state of the art joint contrast enhancement and denoising methods.     

A solution to the deficiencies of image enhancement

Image enhancement can improve the perception of information for human viewers, but there also exist some deficiencies in most image enhancement methods. This paper analyzes the deficiencies and gives a solution to improve image enhancement results—image fusion. In order to overcome the deficiencies well, we present an image fusion method based on a simple and effective evaluation of local contrast. Experimental results demonstrate that image fusion is a feasible solution to the deficiencies of image enhancement, because the deficiencies, like the loss of details, the loss of local contrast and gray-world violation, can be effectively reduced by fusing an enhancement result and an original image.     

Adaptive multi-histogram reversible data hiding with contrast enhancement

Unlike existing reversible data hiding with contrast enhancement (RDHCE) methods, which excessively improve the image contrast for achieving the required capacity, the proposed method improves the image contrast appropriately while providing satisfactory embedding capacity. To this end, an adaptive multi-histogram RDHCE method is proposed in this study to improve the local and global contrast by considering the local properties of the histograms. On the one hand, fuzzy C-means clustering combining multiple features that are deliberately designed for contrast enhancement is employed to generate seven sharply-distributed prediction error histograms (PEHs). Subsequently, the genetic algorithm is utilized to adaptively select the optimal pairs achieving the best embedding performance for each PEH according to the local characteristics of PEH distribution, resulting in improving the local contrast adaptively and embedding significant amount of data. Additionally, two-sided histogram shifting (HS) is utilized to improve the global contrast appropriately while embedding reasonable amount of data. The experimental results demonstrate that the proposed method achieves better local and global contrast while providing a high embedding capacity compared with other existing RDHCE methods.     

一种抗噪的红外图像对比度增强方法

提出一种基于离散平稳小波变换和非线性增益的红外图像对比度增强方法。对红外图像进行离散平稳小波变换后,利用所提出的去噪方法对分辨率较好的各高频子带直接去噪;并利用所提出的非线性增益法结合文中的去噪法对分辨率较差的各高频子带进行增强。实验结果表明,提出的方法在有效地增强红外图像对比度的同时,又能很好地抑制相关噪声。算法在视觉质量上优于传统的反锐化掩膜法、直方图均衡法。     

Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition

A noisy low-light image enhancement algorithm based on structure-texture-noise (STN) decomposition is proposed in this work. We split an input image into structure, texture, and noise components, and enhance the structure and texture components separately. More specifically, we first enhance the contrast of the structure image, by extending a 2D-histogram-based image enhancement scheme based on the characteristics of low-light images. Then, we reconstruct the texture image by retrieving residual texture components from the noise image and enhance it by exploiting the perceptual response of the human visual system (HVS). Experimental results on both synthetic and real-world images demonstrate that the proposed STN algorithm sharpens the texture and enhances the contrast more effectively than conventional algorithms, while providing robust performance under various noise and illumination conditions.     

Strong UV intensity enhancement in ZnO nanorods via surface modification of formic acid

The ZnO nanorods arrays synthesized by the chemical bath deposition method were surface modified by the formic acid at room temperature in a very short time. The optical properties of the samples before and after modification had been investigated in detail. The results showed a remarkable strong enhancement of UV emission intensity by almost a factor of 14 after the formic acid modification, which is mainly attributed to the great suppression of the surface recombination since the formic acid can effectively remove a part of OH bonds on the surface of ZnO nanorods.     

基于小波变换的低对比度图像增强

针对传统算法存在噪声过增强的问题,提出了基于小波分析的图像增强算法。在小波变换多尺度分析的基础上,算法对图像多尺度分解得到的小波系数进行缩减去噪,然后在不同尺度上对各分解系数进行不同程度的增强;对同一尺度的系数进行非线性处理以增加对比度;增强低频子带图像的对比度以保证整体的增强效果。实验表明,该算法能有效地增强低对比度图像,减小了噪声的增强幅度,使结果图像具有很好的视觉效果。     

弱对比度细节模糊增强的自动参数选择

针对广义模糊增强算法中参数选择的主观性,提出了一种模糊参数自动调整的方法。通过分析实验得出模糊参数对图像增强效果的重要性,给出一种用图像可检测边缘度和图像熵的熵值作为图像增强的评价标准,并根据这个标准实现了原来模糊增强算法中参数的自动选择,达到图像的自动增强,提高图像中弱边缘对比度的目的。最后通过实验证明算法的有效性。     

Seam carving based aesthetics enhancement for photos

Recently people are becoming more and more interested in the quality of photographs with the growing interest of image aesthetics. Many previous works start to focus on aesthetically enhancing the quality of images. In this paper, we come up with a novel approach to enhance image aesthetics. An aesthetically beautiful image usually has a clever composition of objects, the optimal positions of which have been deeply discussed by previous methods and reached good performance. After getting the optimal position of the object in images, we try to rearrange all the objects. Instead of picking the object out and pasting it on the suggested place, we propose an improved seam carving approach to change the relative positions of the objects in the image, which is able to move the object to a better place. We adopt the energy function to measure the saliency of each pixel and then find out the seams that should be cut off and inserted. After cutting off unimportant seams by pixel-removing and inserting seams by inpainting, we are able to maintain the resolution of the image as well as enhance the aesthetics in composition. In order to test the effectiveness of our method, we compare the performance of our approach with other state-of-the-art techniques, which well illustrates the satisfying performance of our method.     

A multivariate contrast enhancement technique for multispectralimages

Multispectral and true-color images are often enhanced using histogram-based methods, usually by adjustment of color components after transformation to a selected secondary color system. Enhancement aimed toward the preservation of certain important perceptual qualities generally calls for the secondary coordinate system to be perceptually based. However, independent modification of the secondary components seldom uses the full extent of the RGB gamut unless some color values are clipped at the RGB boundaries. Preserving perceptual attributes is sometimes less important than obtaining the greatest possible color contrast improvement. This is especially true for color composites derived from multispectral images, which have no significant basis in human perception. A new multivariate enhancement technique the authors have named “histogram explosion” is able to exploit nearly the full RGB extent without clipping. While not generally based upon a perceptual model, the method can preserve original hue values when parameters are chosen properly. Experimental results of histogram explosion are presented, along with an analysis of its computational complexity     

一种有效的低对比度水下图像增强算法

针对水下图像光照不均且对比度低的问题,提出一种新的水下图像增强算法。该算法首先利用去背景法获得照明均匀的前景图像;然后对传统的Butterworth同态滤波器进行改进,提高其高频增益并与相角结合,使得前景图像经改进的Butterworth同态滤波器滤波后,低频成分得到很好的抑制而高频成分被有效放大,从而获得高对比度的水下图像。实验结果表明,与其他算法相比,本文算法能有效地提高图像对比度。     

Reversible data hiding with contrast enhancement using adaptive histogram shifting and pixel value ordering

Existing image-based reversible data hiding (RDH) methods tend to focus on increasing embedding capacity, but few consider keeping or improving visual image quality. Wu et al. proposed a new RDH method with contrast enhancement (RDH-CE) by pair-wisely expanding the histogram to the lower end and upper end. RDH-CE is especially valuable in exploiting the details of poorly illustrated images for which the visibility of image details is more important than just keeping PSNR high. However, obvious visual image distortion appears when embedding level gets high, and embedding capacity is relatively low when embedding level is small. In this paper, Wu et al.'s work is improved from three perspectives, namely image contrast enhancement, visual distortion reduction, and embedding capacity increment. The image contrast is improved by making the histogram shifting process adaptive to the histogram distribution characteristics, the image visual distortion is reduced by cutting off half the modification range of pixels induced in histogram pre-shifting, and the embedding capacity is increased by exploiting the pixel value ordering technique at the early stage of data embedment. Experimental result proves that the proposed work is effective in improving image contrast, reducing visual image distortion, and increasing embedding capacity.     

Real-world image dehazing with improved joint enhancement and exposure fusion

In this work, a single image dehazing method that improves the haze removal capacity of the Joint Contrast Enhancement and Exposure Fusion (CEEF) method with Smoothing-Sharpening Image Filter (SSIF) is presented. In this method, the hazy image is first sharpened with SSIF to obtain a sharper image. In this way, the difference between haze and objects is amplified. Then, the AHE procedure in CEEF is replaced by CLAHE to obtain an enhanced CEEF. The enhanced CEEF is applied to the filtering result to obtain the final dehazed image. Observations demonstrate that the proposed method obtains enhanced results while reducing the amount of haze. The visual and quantitative comparisons between the proposed method and state-of-the-art dehazing methods show that the proposed method has better dehazing performance and has a 50% improvement in terms of the FADE metric compared to the closest result.     

基于多尺度对比度塔的图像增强新算法

传统图像增强算法对灰度级比较分散、细节信号分布在整个灰度级空间的图像难以取得令人满意的效果,而且往往在增强图像的同时也使图像的噪声得到了提升。在此针对传统图像增强技术的缺点,提出了一种新的图像增强算法。该算法采用高斯窗口函数对图像进行变换,并通过构造多尺度对比度塔来对图像进行增强。实验结果表明该算法在灰度分散的情况下同样能有效地对细节信号进行增强,同时对图像中的噪声信号也有较好的抑制作用。     

一种自适应提升视频图像对比度的算法

面向数字电视视频后处理芯片中提高对比度的需求,针对传统直方图均衡技术偏离原始平均亮度,放大背景噪声的缺点,提出了一种基于改进的直方图均衡的视频图像对比度提升技术.首先改善统计直方图的方法,将得到的累积直方图限制在一定范围内;然后根据平均亮度计算均衡系数;最后根据累积直方图和均衡系数,得到变换函数曲线并进行映射.仿真结果表明,应用该方法处理后的视频图像,在提升图像对比度的同时,既保持了原始图像的平均亮度,又没有增强背景噪声.     

一种鲁棒的非均匀灰度图像分割算法

针对非均匀灰度图像分割困难及分割效率低下的问题,该文提出了一种基于活动轮廓模型的高效图像分割算法。不同于传统水平集方法中仅用单一信息定义的能量泛函,该算法结合图像的边缘信息和区域统计信息定义了一个新的能量泛函。边缘信息的利用便于演化轮廓线快速精确地定位至物体边缘;区域统计信息由局部统计信息和全局统计信息构成,一方面,局部统计信息的利用能够有效处理图像的灰度分布不均匀现象,另一方面,全局统计信息的利用避免了轮廓线陷入局部极小值。最后,在轮廓线演化过程中,通过高斯卷积核实现快速规则化,避免了传统模型计算代价高昂的重新初始化或规则化。合成图像和真实图像的实验结果表明,该文算法不仅能够快速有效分割灰度分布不均匀的弱边缘物体,而且对于多灰阶复杂结构物体也能够精确分割;同时,该算法对噪声和初始轮廓线具有较好的鲁棒性。     

High enhancement factor of Au nano triangular prism structure for surface enhanced coherent anti-Stokes Raman scattering

Coherent anti-Stokes Raman scattering spectroscopy(CARS) is a well-known detecting tool in biosensing and nonlinear spectroscopy. It can provide a non-invasive alternative without the need for exogenous labels, while the enhancement factor for surface plasmon resonances(SPR) are extensively used to increase the local field close to the oscillators and which can obtain high enhancement. In this work, we investigate the enhancement factor of our structure for surface-enhanced coherent anti-Stokes Raman scattering. The absorption spectrum of the structure has been studied, a wide range of absorption has been realized. The enhancement can be as high as 10¹⁶ over standard CARS. Our design is very useful for improving the enhancement factor of surface-enhanced coherent anti-Stokes Raman scattering.