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.     

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。     

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。     

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     

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。     

基于非局部全变分正则化优化的单幅雾天图像恢复新方法

该文针对无雾图像具有高灰度对比度且大气遮罩局部平滑的特性,提出一种基于非局部全变分正则化优化的单幅雾天图像恢复新方法。先构建一种基于非局部全变分正则化的有约束优化算法对大气遮罩进行估计,然后通过优化Bregman分离迭代法求解非局部Rudin-Osher-Fatemi模型获得准确的大气遮罩,进而从雾天场景图像恢复出场景图像。实验结果表明,所提新方法可以有效地对雾天降质图像进行复原,对多纹理复杂区域的恢复效果也较好。     

Image coarsening by using space-filling curve for decomposition-based image enhancement

We propose a novel space-filling curve based image coarsening method, which automatically extracts a base-layer from an input image while still preserving its structural context, meaningful details, et cetera. In the proposed method, specifically, a one-dimensional edge-preserving smoothing filter, which is called a vector ε-filter, is applied to an input image along a space-filling curve. In this regard, the space-filling curve is constructed by using a minimum spanning tree which extracts the structural context of the input image. This novel image coarsening approach is completely different from all conventional approaches employing any kind of two-dimensional filter window. Furthermore, this coarsening method can effectively produce an aggregation of texture details as well as enhance sharp edges, while preserving structural contexts such as thin lines and sharp corners. The main benefit of the coarsened image by the proposed method is its suitability for extracting fine features of an input image for decomposition-based image enhancement. In this paper, the structural-context-preserving image coarsening capability of the proposed method is verified by some results from experiments and examples. Then we show our new method’s characteristics in practical application to decomposition-based image enhancement by using some other examples.     

MR image enhancement using an extended neighborhood filter

A filtering scheme is proposed for contrast enhancement within a Region-Of-Interest (ROI) containing unconnected foreground regions of narrow spatial extent such as multiple sclerosis, or ischemic lesions in Magnetic Resonance (MR) images. This involves determination of localized multiplicative weights in the spatial domain using an extended set of neighborhood directions. The degree of enhancement is shown to depend on the number of such directions, as determined from the size of a rectangular lattice, together with a threshold value used for computing the multiplicative weights. Best performance in respect of visual quality is achieved by choosing a threshold corresponding to the maximum Contrast Ratio, and lattice size corresponding to the maximum Peak Signal-to-Noise Ratio within the ROI. It is shown that the proposed filter overrides Localized Histogram based Equalization (LHE) based techniques in terms of computational complexity, preservation of structural similarity and attaining the maximum extent of contrast stretching.     

薄云薄雾影响下目标影像自适应增强

卫星图像在拍摄过程中,由于云雾等因素影响,导致影像的亮度增加,目标影像对比度和分辨率下降。针对薄云薄雾对卫星图像的影响,提出目标影像的自适应增强算法。实验表明,该算法具有较强的自适应性。提高了薄云薄雾影响下的目标影像的对比度,恢复了该区域的亮度,同时,保持了薄云薄雾影响弱的区域的对比度和亮度,达到了目标影像自适应增强的目的。     

Adaptive fuzzy contrast factor enhancement technique for low contrast and nonuniform illumination images

This paper presents a new enhancement technique using the fuzzy set theory for low contrast and nonuniform illumination images. A new parameter called the contrast factor which will provide information on the difference among the gray-level values in the local neighborhood is proposed. The contrast factor is measured by both local and global information to ensure that the fine details of the degraded image are enhanced. This parameter is used to divide the degraded image into bright and dark regions. The enhancement process is applied on gray-scale images wherein the modified Gaussian membership function is employed. The process is performed separately according to the image’s respective regions. The performance of the proposed method is comparable with other state-of-the-art techniques in terms of processing time. The proposed method exhibits the best performance and defeats other methods in terms of preserving brightness and details without amplifying existing noises.     

基于各向异性扩散方程的图像对比度增强方法

讨论了光学图像中同时存在噪声与模糊时的对比度增强问题.构造了一种基于边缘定向扩散的各向异性非线性扩散方程来作为图像的光滑约束项,并根据模糊后的图像在边缘处相对清晰图像具有较大误差的事实,构造增强图像与原图像之间的非均匀逼近项,将此两项通过正则化参数联系起来,得到了一种图像对比度增强的正则化模型,并利用Euler方程将该模型转换成一种可以快速求解的各向异性非线性扩散模型.该模型能在抑制噪声的同时增强图像的边缘,在模型的解算上也不存在大型矩阵的存储与运算问题.数值计算结果表明,新方法适合于多种形式的模糊和不同程度的噪声污染,相对现有方法具有更好的边缘锐化能力和更高的清晰度,峰值信噪比比现有方法提高了1～2 dB,边缘保护指数也比现有方法有较大提高.     

基于多重分形的红外图像增强技术

红外图像边缘模糊,对比度较低,不适合人眼观察,应该对其进行增强.但是,以往的增强方法对噪声增强过度,使细节失真,且未考虑人眼的视觉特性,视觉效果不够好.提出用多重分形理论对红外图像进行分析,提取了红外图像的多重分形奇异指数和多重分形谱特征.分析得到了图像每个像素的分形特征数据,利用人眼的视觉敏感特征把图像的像素分为平滑区、纹理区和边缘区.人眼视觉空间频率特征对图像细节的边缘区域比较敏感,利用这一特性对图像加权增强.最后,进行了计算机仿真实验,实验结果表明:该方法能够突显人眼敏感的图像区域,解决红外图像边缘模糊的问题,使增强图像更适合人眼观察.     

Image enhancement using divide-and-conquer strategy

Existing enhancement methods tend to overlook the difference between image components of low-frequency and high-frequency. However, image low-frequency portions contain smooth areas occupied the majority of the image, while high-frequency components are sparser in the image. Meanwhile, the different importance of image low-frequency and high-frequency components cannot be precisely and effectively for image enhancement. Therefore, it is reasonable to deal with these components separately when designing enhancement algorithms with image subspaces. In this paper, we propose a novel divide-and-conquer strategy to decompose the observed image into four subspaces and enhance the images corresponding to each subspace individually. We employ the existing technique of gradient distribution specification for these enhancements, which has displayed promising results for image naturalization. We then reconstruct the full image using the weighted fusion of these four subspace images. Experimental results demonstrate the effectiveness of the proposed strategy in both image naturalization and details promotion.     

Low light image enhancement with dual-tree complex wavelet transform

In low light condition, low dynamic range of the captured image distorts the contrast and results in high noise levels. In this paper, we propose an effective contrast enhancement method based on dual-tree complex wavelet transform (DT-CWT) which operates on a wide range of imagery without noise amplification. In terms of enhancement, we employ a logarithmic function for global brightness enhancement based on the nonlinear response of human vision to luminance. Moreover, we enhance the local contrast by contrast limited adaptive histogram equalization (CLAHE) in low-pass subbands to make image structure clearer. In terms of noise reduction, based on the direction selective property of DT-CWT, we perform content-based total variation (TV) diffusion which controls the smoothing degree according to noise and edges in high-pass subbands. Experimental results demonstrate that the proposed method achieves a good performance in low light image enhancment and outperforms state-of-the-art ones in terms of contrast enhancement and noise reduction.     

红外图像序列的目标增强和检测

文中主要研究了远距离红外图像序列的目标增强和检测问题,提出了基于局部纹理特征的红外图像增强算法,利用红外图像目标和背景区域局部纹理特征的差异来增强目标和背景区域的对比度;为了提高目标检测的速度和精度,利用序列图像的帧间相关信息,采用边检测边跟踪边确认的目标检测方法。实验结果表明,对于远距离、小目标的红外图像,这种增强算法在目标对比度和细节方面都明显优于直方图均衡化方法,而本文采用的目标检测方法仅需很少几帧图像就能检测和跟踪目标,可以在确保目标检测的可靠性的前提下,提高目标的检测速度。     

Wireless capsule endoscopy images enhancement via adaptive contrast diffusion

Wireless capsule endoscopy (WCE) has been widely applied to diagnose diseases in human digestive tract due to its advantage that it can directly view the entire small intestine for the first time. However, many WCE images are rather dark, which challenge to analysis and diagnosis exerted by a clinician. To overcome this shortcoming so as to assist physicians, especially computer aided detection, we propose an adaptive contrast diffusion to enhance WCE images. Based on local analysis of WCE images, we put forward a new idea of contrast diffusion. Then, we employ contrast diffusion to enhance WCE images with an adaptive choice of the conductance parameter, which plays an important role in diffusion. Extensive experiments demonstrate that this new method exhibits promising performance of enhancement for WCE images, leading into a better visualization as well as an improved classification performance of WCE images using computerized methods.     

红外血流仪红外发射强度自锁定系统的设计与分析

简单介绍了红外血流仪的构成及原理，着重对红外发射强度自锁定系统进行设计和分析。虽然每个人的血液浓度和流速均不同，现已找到一种能自动调节红外发射强度，使输出波形幅度趋于一致的方法。     

Context enhancement through infrared vision: a modified fusion scheme

In the night vision applications, visual and infrared images are often fused for an improved awareness of situation or environment. The fusion algorithms can generate a composite image that retains most important information from source images for human perception. The state of the art includes manipulating in the color spaces and implementing pixel-level fusion with multiresolution algorithms. In this paper, a modified scheme based on multiresolution fusion is proposed to process monochrome visual and infrared images. The visual image is first enhanced based on corresponding infrared image. The final result is obtained by fusing the enhanced image with the visual image. The process highlights the features from visual image, which is most suitable for human perception.