Differentiation-Based Edge Detection Using the Logarithmic Image Processing Model

The logarithmic image processing (LIP) model is a mathematical framework which provides a specific set of algebraic and functional operations for the processing and analysis of intensity images valued in a bounded range. The LIP model has been proved to be physically justified by that it is consistent with the multiplicative transmittance and reflectance image formation models, and with some important laws and characteristics of human brightness perception. This article addresses the edge detection problem using the LIP-model based differentiation. First, the LIP model is introduced, in particular, for the gray tones and gray tone functions, which represent intensity values and intensity images, respectively. Then, an extension of these LIP model notions, respectively called gray tone vectors and gray tone vector functions, is studied. Third, the LIP-model based differential operators are presented, focusing on their distinctive properties for image processing. Emphasis is also placed on highlighting the main characteristics of the LIP-model based differentiation. Next, the LIP-Sobel based edge detection technique is studied and applied to edge detection, showing its robustness in locally small changes in scene illumination conditions and its performance in the presence of noise. Its theoretical and practical advantages over several well-known edge detection techniques, such as the techniques of Sobel, Canny, Johnson and Wallis, are shown through a general discussion and illustrated by simulation results on different real images. Finally, a discussion on the role of the LIP-model based differentiation in the current context of edge detection is presented.     

一种基于LIP的PDE图像复原新模型

以对数图像处理(LIP)数学模型为基础,结合全变分(TV)图像复原方法,提出了一种新的图像复原模型,称之为LIP_TV模型。该模型弥补了经典图像复原方法的不足,并具有与人眼视觉特征相吻合的特点。实验表明,与经典图像复原方法相比,该模型不仅复原效果更好,而且能很好地保持图像的边缘纹理特征。     

A Geometric Model of Brightness Perception and Its Application to Color Images Correction

Human perception involves many features like contours, shapes, textures, and colors to name a few. Whereas several geometric models for contours, shapes and textures perception have been proposed, the geometry of color perception has received very little attention, possibly due to the fact that our perception of colors is still not fully understood. Nonetheless, there exists a class of mathematical models, gathered under the name Retinex, which aim at modeling the color perception of an image, which are inspired by psychophysical/physiological knowledge about color perception, and which can geometrically be viewed as the averaging of perceptual distances between image pixels. Some of the Retinex models turn out to be associated with an efficient image processing technique for the correction of camera output images. The aim of this paper is to show that this image processing technique can be improved by including more properties of the human visual system. To that purpose, we first present a generalization of the perceptual distance between image pixels by considering the parallel transport map associated with a covariant derivative on a vector bundle, from which can be derived a new image processing model for color images correction. Then, we show that the family of covariant derivatives constructed in Batard and Sochen (J Math Imaging Vis 48(3):517–543 2014) can model some color appearance phenomena related to brightness perception. Finally, we conduct experiments in which we show that the image processing techniques induced by these covariant derivatives outperform the original approach.     

人眼灰度感知建模及其在图像增强中的应用

原直方图均衡化算法处理结果不理想,主要是图像灰度域上实现的均衡化并不代表人眼感知亮度域上的均衡化,对此提出人眼感知亮度域上的直方图均衡化算法,但由于使用的灰度人眼感知模型不准确,处理效果也没有得到明显的改善。提出一种灰度人眼感知模型的建模方法,利用实验测试的方法得到人眼视觉系统临界可见偏差曲线;再推导得到人眼对不同灰度背景下同等灰度差别的敏感度曲线;接着利用上一步结果再通过积分和归一化方法得到灰度人眼感知模型;根据以上灰度人眼感知模型对直方图均衡化算法进行改进。对比实验结果表明,提出算法相比于原直方图均衡化具有明显的改善效果,相比于CLAHE、BBHE以及HMF等直方图改进算法,也具有无需参数调节,增强效果显著和适应性强等优点。     

An Input/Output Semantics for Distributed Program Equivalence Reasoning

A new notion of input/output equivalence of distributed imperative programs, with synchronous communications, is introduced. It preserves the input/output relation, encompassing both, initial/final state and communication channel values. For its mathematical justification, the semantic framework of Manna and Pnueli, based on finite transition systems and reduced behaviors, is extended with the notion of input/output behavior. A set of laws for the equivalence is overviewed. A deduction rule for the substitution of references to input/output equivalent procedures is defined and justified in the new semantics. The rule is applied to decompose distributed program simplification proofs, introduced in a prior work, which use the laws to establish the equivalence between a sequential and a parallel communicating program. They include communication elimination as one of their steps. An outline of one of such proofs, for a pipelined processor model, is included.     

图像增强中分数阶阶次自适应模型的构造

为了解决分数阶微分算子在图像增强中需要人工寻找最佳阶次,缺乏阶次自适应的问题,构造了分数阶微分阶次自适应数学模型。该模型以反正切函数为原型,以图像的梯度信息、局部信息熵、亮度和对比度为自变量,建立了微分阶次与图像局部信息之间的关系,从而可以根据图像的局部信息特征自动计算图像中各个像素点的最佳阶次,并将该模型应用在分数阶微分Tiansi算子的图像增强中。为了验证该模型的有效性,选用标准图像库中的多幅纹理图像进行实验,对实验结果进行了定性和定量分析,在定量分析中采用图像信息熵、平均梯度、清晰度和对比度四个评价指标衡量图像增强的效果,并与二阶微分Laplacian算子、Tiansi算子进行比较。理论分析和实验结果均表明建立该模型的有效性,对灰度图像可以得到连续变化的增强效果,接近于最佳分数阶微分增强效果,符合人们的视觉感受。     

Modified differential evolution algorithm for contrast and brightness enhancement of satellite images

Satellite images normally possess relatively narrow brightness value ranges necessitating the requirement for contrast stretching, preserving the relevant details before further image analysis. Image enhancement algorithms focus on improving the human image perception. More specifically, contrast and brightness enhancement is considered as a key processing step prior to any further image analysis like segmentation, feature extraction, etc. Metaheuristic optimization algorithms are used effectively for the past few decades, for solving such complex image processing problems. In this paper, a modified differential Modified Differential Evolution (MDE) algorithm for contrast and brightness enhancement of satellite images is proposed. The proposed algorithm is developed with exploration phase by differential evolution algorithm and exploitation phase by cuckoo search algorithm. The proposed algorithm is used to maximize a defined fitness function so as to enhance the entropy, standard deviation and edge details of an image by adjusting a set of parameters to remodel a global transformation function subjective to each of the image being processed. The performance of the proposed algorithm is compared with ten recent state-of-the-art enhancement algorithms. Experimental results demonstrate the efficiency and robustness of the proposed algorithm in enhancing satellite images and natural scenes effectively. Objective evaluation of the compared methods was done using several full-reference and no-reference performance metrics. Qualitative and quantitative evaluation results proves that the proposed MDE algorithm outperforms others to a greater extend.     

融合结构与非结构信息的自然图像恰可察觉失真阈值估计

目的 研究表明,图像的恰可察觉失真（JND）阈值主要与视觉系统的亮度适应性、对比度掩模、模块掩模以及图像结构等因素有关。为了更好地研究图像结构对JND阈值的影响,提出一种基于稀疏表示的结构信息和非结构信息分离模型,并应用于自然图像的JND阈值估计,使JND阈值模型与人眼视觉系统具有更好的一致性。方法 首先通过K-均值奇异值分解算法（K-SVD）得到过完备视觉字典。然后利用该过完备字典对输入的自然图像进行稀疏表示和重建,得到该图像对应的结构层和非结构层。针对结构层和非结构层,进一步设计基于亮度适应性与对比度掩模的结构层JND估计模型和基于亮度对比度与信息不确定度的非结构层JND估计模型。最后利用一个能够刻画掩模效应的非线性可加模型对以上两个分量的JND估计模型进行融合。结果 本文提出的JND估计模型利用稀疏表示将自然图像的结构/非结构信息进行分离,然后采用符合各自分量特点的JND模型进行计算,与视觉感知机理高度一致。实验结果表明,本文JND模型能够有效地预测自然图像的JND阈值,受污染图的峰值信噪比（PSNR）值比其他3个JND对比模型值高出35 dB。结论 与现有模型相比,该模型与人眼主观视觉感知具有更好的一致性,更能有效地预测自然图像的JND阈值。     

基于人眼视觉感知驱动的井下图像增强算法

为解决传统的对比度增强方法在对井下图像进行处理时不能兼顾压缩动态范围、调整亮度以及增强图像对比度等问题,提出一种基于人眼视觉感知特性的井下图像对比度增强算法。首先根据人眼亮度掩蔽特性对图像进行区域划分,然后基于非线性亮度映射模型,对图像的不同区域进行不同尺度的非线性调整,最后再将亮度调整后的不同区域组合成新的图像。实验结果表明,该方法能有效增强井下低照度图像的对比度,提升图像的视觉效果。     

基于视觉注意力和局部复杂性的图像隐写算法

视觉注意力是人类感知系统中一个十分重要的特性,但目前基于人类视觉感知机理的隐写算法大多只考虑亮度、对比度和掩蔽效应等低层因素.文中将视觉注意力模型引入隐写算法中,提出一种新的基于视觉注意力和局部复杂性的图像隐写算法.算法先采用均方差分析图像的局部复杂性,在复杂性较大的区域引入Itti模型构造注意力显著图,进而利用视觉熵来定量刻画注意力特性,并将图像分块处理,图像块按照不同的局部复杂程度和注意力等级分成3种类型,最终利用LSB方法进行隐写.大量实验结果表明,文中算法在嵌入大量信息后仍能保持较好的视觉感知效果,且能抵抗一类通过直方图对比的隐写分析方法.     

多尺度权重评估的MSRCR混合曝光成像算法

针对混合曝光成像算法过程中会出现低曝光处细节丢失且颜色失真饱和度不佳导致视觉观感下降的问题,提出一种多尺度权重评估的MSRCR（Multi-Scale Retinex with Color Restoration,MSRCR）混合曝光融合算法。基于Retinex模型将待融合图像分解为亮度分量与反射光分量,对亮度分量结合ACES函数构造光照补偿归一化函数进行处理,对反射光分量加入颜色恢复函数提升色彩细节;分别从曝光量、饱和度、对比度、色域四个尺度设计图像融合权重值,通过多尺度评估优化融合比例;利用Laplacian金字塔融合算法进行多尺度权重融合获得最终图像。实验结果表明,与传统的图像融合算法相比,该算法处理效果较好,有效降低了暗处失真率,提升了视觉信息保真度。     

颜色的模糊识别方法及其在图像检索中的应用

基于内容的图像检索（Content-based Image Retrieval,CBIR）以其极高的理论与应用价值成为了图像处理领域的研究热点。提取和匹配图像特征是CBIR的主要手段。然而提取图像的有效特征是极其困难的。利用HSV颜色空间特性以及人类对颜色的感知规律,提出一种颜色识别方法。应用此方法对图像的像素进行一种保持结构的分类,并在类内提取结构特征。图像的特征匹配将在同类像素集合间进行,降低了图像特征提取与匹配的复杂性。实验表明,提出的图像检索方法有良好的效果。     

基于流形特征相似度的感知图像质量评价

图像质量评价（Image quality assessment, IQA）的目标是利用设计的计算模型得到与主观评价一致的结果,而人类视觉感知特性是感知图像质量评价的关键.大量研究发现,认知流形和拓扑连续性是人类感知的基础即人类感知局限在低维流形之上.基于图像低维流形特征分析,本文提出了基于流形特征相似度（Manifold feature similarity, MFS）的全参考图像质量评价方法.首先,利用正交局部保持投影算法来模拟大脑的视觉处理过程获取最佳映射矩阵进而得到图像的低维流形特征,通过流形特征的相似度来表征两幅图像的结构差异,从而反映感知质量上的差异.其次,考虑亮度失真对人眼视觉感知的影响,通过图像块均值计算亮度相似度并用于评价图像的亮度失真;最后,结合两个相似度得到图像的客观质量评价值.在四个公开图像测试库上的实验结果表明,所提出方法与现有代表性的图像质量方法相比总体上具有更好的评价结果.     

Expert system for low frequency adaptive image watermarking: Using psychological experiments on human image perception

This paper proposes a new system for low frequency adaptive image watermarking based on the statistical data from psychological experiments on human image perception. The new approach can lead to a reduction of degrading the subjective image quality that often occurs when watermark is embedded into low frequency area. In order to reduce the degrading of image quality, the new approach determines the strength of watermark according to local image characteristics such as brightness and contrast. By conducting a behavioral experiment on human image fidelity based on the psycho-visual image association technique, we were able to infer the relationship between the watermark strength and the different levels of image brightness and contrast information. The exact watermark is extracted according to edge characteristics by adopting a so-called edge mask that exploits the coefficients of subbands in the subsampled discrete wavelet transform images. Thus, our new approach does not require original images for watermark. We also show the new approach is practically validated against some standard images.     

Asplünd׳s metric defined in the Logarithmic Image Processing (LIP) framework: A new way to perform double-sided image probing for non-linear grayscale pattern matching

The present paper focuses on non-linear pattern matching based on the Logarithmic Image Processing (LIP) Model. Our contribution consists first of using the scalar multiplication defined in the LIP context to extend the little-known Asplünd?s metric to gray level images. Such a metric is explainable as a novel technique of double-sided image probing and presents the decisive advantage of being physically justified in the field of transmitted light acquisition. Moreover, thanks to the consistency of the LIP context with human vision, Asplünd?s metric is also applicable to images acquired in reflected light: in fact, plenty of image processing algorithms aim at extracting information as a human eye would do. Finally, the proposed approach is particularly efficient in the presence of lighting variations or lighting drift. In the paper, we also suggest a solution to overcome the main drawback of probing techniques, which resides in a high sensitivity to noise. Various examples are presented to highlight the efficiency of the method.     

Rendering of HDR images to improve brightness discrimination

The impression of quality of images can be enhanced on a high dynamic range (HDR) displays. Generally, a conventional 8‐bit image can be processed to an HDR image by inverse tone mapping operators. Among the operators, brightness discrimination mapping by applying brightness adaptation model attempted to mimic the human visual system. In this paper, we use a brightness adaptation model to derive a brightness discrimination mapping algorithm for HDR displays. The proposed algorithm maximizes a function, which represents the local and global brightness discrimination range by exploiting characteristics of the human visual system. Enhancement of details is verified by visualizing HDR images from dark to bright regions. Improvement of dynamic range is quantified by measuring increased discrimination ratio.     

The symmetric logarithmic image processing model

In this paper, a new extension of logarithmic image processing (LIP) model, called Symmetric Logarithmic Image Processing (SLIP), is proposed. Inspired by the previously developed symmetric models, the SLIP model defines a vector space on a symmetric bounded set. The development is aimed at (1) maintaining the physical interpretation of the LIP model and (2) solving the potential out-of-range problem which the LIP model has. The SLIP model is also able to deal with transmitted and reflected light images. The advantage of the SLIP is demonstrated through an image enhancement application using the generalized unsharp masking algorithm.     

用于数字图像直方图处理的一种二值映射规则

直方图表示数字图像中每一灰度级与其出现频数间的统计关系，它可给出图像的概貌性描述，而基于直方图修改技术的灰度变换是图像增强的实用而有效的处理方法之一。直方图处理包含均衡化和规定化两种技术。均衡化的目的是使图像像素均匀地分布在所有灰度级上；规定化的目的是将原图像的直方图转变为规定的直方图，以便突出一定灰度范围内的图像。为了进一步提高直方图处理算法的有效性，首先分析了现有的几种数字图像直方图均衡化和规定化算法存在的缺点，然后提出了一种新的二值映射规则(BML)，该规则基于最优控制原理，以直方图误差最小为准则进行灰度映射，实验证明．该规则算法简单，无论是用于直方图均衡化处理．还是用于直方图规定化处理，均较其他映射规则都更为有效。     

Design Considerations for Digital Image Processing Systems

The brightness levels in a scene or image, together with the spatial relationships among these levels, comprise the total input and output data available to the digital image processor. Indeed, in any mathematical image processing operation, these are the elements of the image which are being manipulated and which represent the major concern of the image processing professional. However, many other factors affect image brightness relations and can distort or obscure the outcome of any image processing experiment. These factors, which consist of a long chain of transmitters, transducers, signal conditioners and processors, are in aggregate commonly called the image chain. An understanding of the image chain is essential to the design of image processing systems.     

一种车牌号码图像二值化的新方法

利用数学形态学的开运算消除图像光照不均匀影响,根据欧几里德距离确定图像阈值。基于人类视觉特性确定每个像素点的视觉敏感度系数,并根据图像像素点灰度值、图像阈值和视觉敏感度系数对图像进行二值化处理。该方法能有效地消除伪影,较好地保护字符笔画。模拟实验结果表明,该方法是有效的。