基于彩色图像的增强算法

本文在分析常用锐化算法的基础上，对其进行改进，提出了一种新的针对彩色图像的边缘增强算法，并用模糊集理论选取阈值去除噪声。实验证明，该算法能有效地突出彩色图像的边缘，与其它算法比较，该算法耗时很少。     

A nonlinear image contrast amplifier

Thermal picosecond light scattering involving the amplification of weak beams interfering with a strong beam in absorbing liquid media provides a mechanism for amplifying the contrast of weak optical images.     

Color image sharpening based on local color statistics

This paper presents an effective color image sharpening method, which is based on local color statistics. First, the variance of a set of color samples is measured by a scalar that is computed based on the sum of distances of color vectors, whereas other studies usually treat a color variance as a 3D vector. This is because what a variance expresses is the degree of the deviation of the image (vector) signal from its mean, indicating that describing this degree of deviation by a scalar is reasonable. Then, the local scalar variance and mean vector are combined together to measure the change of color image signal from a pixel to its neighboring ones, and the polarity of the change is determined by the change of luminance. Finally, based on the measure of the change, an effective sharpening operator is developed. Experimental results show that the proposed method excellently sharpens different kinds of color images and at the same time preserves image chromaticity well, and outperforms other typical sharpening techniques in both objective assessment and visual evaluation.

     

An adaptive image sharpening scheme based on local intensity variations

Unsharp masking-based approaches are widely used in consumer electronics and printing technology for increasing the sharpness of the image. In the classical approaches, such improvements are achieved by adding the high-frequency details to the underlying image without considering any noise present in the image. As a result, such approaches yield visually poor results on noise-deteriorated images. In this paper, we propose an adaptive unsharp masking scheme which can tolerate the noise content, i.e., proposed algorithm will perform sharpening operation on the required regions thereby reducing the visual effects of the noise. Experimentally, it has been found out that the proposed approach yields better visual results than classical unsharp masking approach in the presence of noise.     

基于双注意力机制的雾天图像清晰化算法研究

针对传统去雾算法容易依赖先验知识以及恢复出来的清晰图像会产生颜色失真等问题,本文提出一种基于双注意力机制的雾天图像清晰化算法。首先将雾图输入编码器,经过下采样后得到特征图像;特征提取模块将多个特征提取基本块联结在一起,每个基本块由局部残差学习和特征注意模块组成,提高图像质量以及图像特征信息的利用率,增加网络训练的稳定性;然后通过通道注意力与多尺度空间注意力并行的结构处理特征图像,使得网络更加关注细节特征,提取更多关键信息,同时提高网络效率;最后将融合后的特征图像输入解码器中,经过多级映射,得到与输入大小匹配的雾密度图。实验结果表明,不论是对合成雾天图像或者真实雾天图像,本文算法能够高效地进行去雾处理,得到更自然的清晰图像。     

基于对比度的小波图像融合算法研究

图像融合可以集成多个源图像中的冗余和互补信息,增加图像理解的全面性,获得对同一场景的更为准确可靠的图像描述。传统的小波图像融合方法的融合规则是分别对低频和高频系数进行处理,忽略了两者之间的联系。本文提出利用对比度将高频系数和低频系数的处理联系起来,并以对比度作为度量系数取舍的准则进行图像融合。实验仿真研究表明,本文提出的算法很好地保留了多幅源图像的有用信息,是一种有效的图像融合算法。     

引入非线性变换的分形图像压缩编码

提出了一种基于非线性变换的分形图像压缩编码方法。证明了引入的非线性变换满足压缩迭代映射理论。模拟实验的结果表明 ,与传统的分形图像压缩编码方法相比 ,压缩比和峰值信噪比均有所改善。     

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。     

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。     

A fast fractal image coding based on kick-out and zero contrast conditions

A fast algorithm for fractal image coding based on a single kick-out condition and the zero contrast prediction is proposed in this paper. The single kick-out condition can avoid a large number of range-domain block matches when finding the best matched domain block. An efficient method for zero contrast prediction is also proposed, which can determine whether the contrast factor for a domain block is zero or not, and compute the corresponding difference between the range block and the transformed domain block efficiently and exactly. The proposed algorithm can achieve the same reconstructed image quality as the exhaustive search, and can greatly reduce the required computation or runtime. In addition, this algorithm does not need any pre-processing step or additional memory for its implementation, and can combine with other fast fractal algorithms to further improve the speed. Experimental results show that the runtime is reduced by about 50% of that of the exhaustive search method. When combined with the DCT Inner Product algorithm, the required runtime for the algorithm can be further reduced by about 50%. The proposed algorithm was also compared to two other fast fractal algorithms. Experimental results also show that our algorithm achieves a better efficiency and requires a much smaller amount of memory for implementation.     

A new approach to image copy detection based on extended feature sets.

Conventional image copy detection research concentrates on finding features that are robust enough to resist various kinds of image attacks. However, finding a globally effective fealure is difficult and, in many cases, domain dependent. Instead of imply extracting features from copyrighted images directly, we propose a new framework called the extended feature set for detecting copies of images. In our approach, virtual prior attacks are applied to copyrighted images to generate novel features, which serve as training data. The copy-detection problem can be solved by learning classifiers from the training data, thus, generated. Our approach can be integrated into existing copy detectors to further improve their performance. Experiment results demonstrate that the proposed approach can substantially enhance the accuracy of copy detection.     

图像能量与对比度的霾检测算法

针对目前霾检测方法实时性差且成本较高的问题,提出一种基于图像能量与对比度的霾检测方法。首先,对CMOS相机拍摄的图像进行预处理。由于相机受外界影响会出现轻微摆动,故需对图像进行配准;其次,在图像的关键区域中获取目标与水平天空背景的对比度和图像能量两个特征向量;再次,将对比度、图像能量、环境湿度作为输入,将激光粒子计数器测得的实时PM10浓度作为输出,进行支持向量回归训练,建立图像和PM10浓度间的关系模型;最后,根据得到的模型计算待测图像所对应的PM10浓度。将该方法检测的PM10浓度与激光粒子计数器测得浓度值进行对比,实验表明该方法检测结果的平均相对误差在10%以内,MSE为0.006 2,表明预测值与真值拟合程度较好,模型检测的精度较高。在此基础上增加训练样本可进一步提高模型精度。此外,该方法可针对不同待测环境建立相应的关系模型,具有较强的灵活性。     

Adaptive image contrast enhancement based on human visual properties

Existing methods for image contrast enhancement focus mainly on the properties of the image to be processed while excluding any consideration of the observer characteristics. In several applications, particularly in the medical imaging area, effective contrast enhancement for diagnostic purposes can be achieved by including certain basic human visual properties. Here the authors present a novel adaptive algorithm that tailors the required amount of contrast enhancement based on the local contrast of the image and the observer's Just-Noticeable-Difference (JND). This algorithm always produces adequate contrast in the output image, and results in almost no ringing artifacts even around sharp transition regions, which is often seen in images processed by conventional contrast enhancement techniques. By separating smooth and detail areas of an image and considering the dependence of noise visibility on the spatial activity of the image, the algorithm treats them differently and thus avoids excessive enhancement of noise, which is another common problem for many existing contrast enhancement techniques. The present JND-Guided Adaptive Contrast Enhancement (JGACE) technique is very general and can be applied to a variety of images. In particular, it offers considerable benefits in digital radiography applications where the objective is to increase the diagnostic utility of images. A detailed performance evaluation together with a comparison with the existing techniques is given to demonstrate the strong features of JGACE.     

A robust approach to image enhancement based on fuzzy logic

In this paper, we propose a robust approach to image enhancement based on fuzzy logic that addresses the seemingly conflicting goals of image enhancement: (i) removing impulse noise, (ii) smoothing out nonimpulse noise, and (iii) enhancing (or preserving) edges and certain other salient structures. We derive three different filters for each of the above three tasks using the weighted (or fuzzy) least squares (LS) method, and define the criteria for selecting each of the three filters. The criteria are based on the local context, and they constitute the antecedent clauses of the fuzzy rules. The overall result of the fuzzy rule-based system is the combination of the results of the individual filters, where each result contributes to the degree that the corresponding antecedent clause is satisfied. This approach gives us a powerful and flexible image enhancement paradigm. Results of the proposed method on several types of images are compared with those of other standard techniques.     

多层级及对比度提升的红外和可见光图像融合

提出了一种针对红外和可见光图像的有效融合算法。首先,在Petrovi?多层级图像融合体系的基础上,在特征级信息中加入高频边缘分量,提高了融合质量;其次,分析了图像特点及目标的物理特性,提出了针对融合结果的对比度提升方法,进一步了提高融合质量。通过主观观察以及客观指标对比证明,该方法获得了优于其他比较方法的融合结果,并且对比度提升方法明显提高了各类方法的红外和可见光图像融合质量,效果好,具有广泛的适用性。     

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。     

基于局部对比度优化的教学视频图像增强方法

针对录制的教学视频图像对比度低或设备引起的伪薄雾情况,提出一种局部对比度优化的增强方法。在反锐化掩模方法的基础上对图像分块进行对比度增强。为求取最大对比度,设定增益因子的约束条件,并求得最优值。同时为解决分块增强带来的块效应,又采用引导滤波对增益因子进行优化。实验结果表明,该方法能有效地对降质的教学视频图像进行增强,具有较好的增强效果和效率。     

限制对比度的多层POSHE自适应图像增强算法

部分子块重叠直方图均衡算法(POSHE)是一种常用的图像增强方法,由于该算法存在过增强和块效应问题,不利于目标的识别和跟踪。为解决该问题,提出限制对比度的多层POSHE图像增强方法(CLMPOSHE),通过对每层子块对比度自适应限制来消除过增强现象,然后采用多层POSHE增强处理,最后将每层改进的POSHE处理结果自适应加权融合。大量实验仿真表明,该方法能有效增强雾天图像。     

一种基于PCNN的图像去噪新方法

该文深入研究了如何用一种有生物学依据的人工神经网络脉冲耦合神经网络(PCNNPulseCoupled Neural Network)进行二值图像去噪与图像平滑,并提出了基于PCNN的图像去噪算法。计算机仿真结果表明,使用PCNN可有效地恢复被噪声污染的二值图像,且恢复图像的信噪比增量高于用另两种常用的图像恢复方法(中值滤波与均值滤波)得到的结果。     

A simple unsupervised MRF model based image segmentation approach

A simple technique has been suggested to obtain optimal segmentation based on tonal and textural characteristics of an image using the Markov random field (MRF) model. The technique takes an initially over segmented image as well as the original image as its inputs and defines an MRF over the region adjacency graph (RAG) of the initially segmented regions. A tonal-region based segmentation technique due to Kartikeyan and Sarkar (1989) has been used for initial segmentation. The energy function has been defined over the first order cliques of the MRF. The essence of this approach is primarily based on quantitative values of the second order statistics, on region characteristics and consequently deciding upon the action of merging neighboring regions using the F-statistic. The effectiveness of our approach is demonstrated with wide variety of real life examples viz., indoor, outdoor and satellite and a comparison of its output with that of a previous work in the literature has been provided.