Remote sensing image enhancement based on the combination of adaptive nonlinear gain and the PLIP model in the NSST domain

To enhance image detail and contrast effectively, we present a novel enhancement method for remotely sensed images. This method is based on the combination of adaptive nonlinear gain and the parameterized logarithmic image processing model (PLIP) in the nonsubsampled shearlet transform (NSST) domain. The algorithm works in several stages by deconstructing the image into low- and high-frequency components, applying different functions to each set of frequency components, and then applying further enhancement functions to the reconstructed image. The experimental results show that the proposed method performs well in terms of definition gain, the contrast improvement index (CII) and the measure of enhancement by entropy (EMEE) when compared to several state-of-the-art image enhancement algorithms, including the nonsubsampled contourlet transform (NSCT) with fuzzy field enhancement, the NSCT with unsharp masking, the feature-linking model, linking synaptic computation for image enhancement and improved fuzzy contrast in the NSST domain.

     

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.     

IMECO: A Reconfigurable FPGA-based Image Enhancement Co-Processor Framework

In this paper, we present a way to improve the computational speed of image contrast enhancement using low-cost FPGA-based hardware primarily targeted to X-ray images. In particular, we consider an enhancement method that consists of filtering followed by histogram modification. Filtering is done via the high boost filter (HBF) which is based on unsharp masking, and the histogram modification which is based on global histogram equalization (GHE). An image enhancement co-processor, IMECO, concept is proposed that enables efficient hardware implementation of enhancement procedures and hardware/software co-design to achieve high-performance low-cost solutions. The co-processor runs on an FPGA prototyping ISA-bus board. At this stage it consists of two hardware functional units that implement HBF and GHE and can be downloaded onto the board sequentially or reside on the board at the same time. These units represent an embryo of virtual hardware units that form a library of image enhancement algorithms. These algorithms can be easily integrated into software templates. In our trials with chest X-ray images, performance improvement over software-only implementations is more than two orders of magnitude, thus providing real-time or near-real-time image enhancement as required in target applications.     

Fuzzy intensification operator based contrast enhancement in the compressed domain

With the increasing sizes of high resolution images, their storage and processing directly in the compressed domain has significantly gained importance. Algorithms for compressed domain image processing provide a powerful computational alternative to classical (pixel level) based implementations. While linear algorithms can be applied straightforward to the JPEG compressed images, this is not the case for nonlinear image processing, as for example contrast enhancement algorithms. In this paper a new implementation in the compressed domain of a very efficient contrast enhancement, based on fuzzy set modeling and on a fuzzy intensification operator, is presented. The fuzzy set parameters are adaptively chosen by analyzing the statistics of the image data in the compressed domain, in order to optimally enhance the image contrast. The nonlinear enhancement procedure requires a grey level threshold, for which an adaptive implementation, taking into account the frequency content of each coefficient block in the DCT (Discrete Cosine Transform) encoded JPEG image is proposed. This guarantees the optimal quality at minimum computational cost. The experimental results for a set of various contrast images validate the good performance and functionality of the proposed implementation.     

Acoustic image enhancement using Gaussian and laplacian pyramid – a multiresolution based technique

Acoustic images captured by side scan sonar are normally affected by speckle noise for which the enhancement is required in different domain. The underwater acoustic images obtained using sound as a source, basically contain seafloor, sediments, living and non-living resources. The Multiresolution based image enhancement techniques nowadays play a vital role in improving the quality of the low resolution image with repeated patterns. Image pyramid is the representation of an image at various scales. In this work, a three level Gaussian and Laplacian pyramids are constructed to represent the image in different resolution. The multiscale representation requires different filters at different scales. The contrast of each image in Gaussian and Laplacian pyramids are improved by applying both histogram equalization and unsharp masking method. The sharpened images are used to reconstruct the enhanced image. The performance measure, peak signal to noise ratio proves that the unsharp masking method applied to difference images of Laplacian pyramid outperforms the other image enhancement methods.     

沙尘降质图像清晰化算法

目的 沙尘环境中获取的图像存在颜色失真、对比度低等问题,不利于人眼辨识以及进一步的图像处理。为解决沙尘降质图像的这些问题,提出一种新的基于颜色调整和对比度增强的沙尘降质图像的增强算法。方法 沙尘降质图像增强要解决两个问题,即颜色偏移和对比度增强。基于沙尘降质图像的的颜色直方图存在的集中性、顺序性以及偏离性等特性,使用高斯模型分别对各通道颜色进行建模,进而进行颜色调整。针对颜色调整后的图像存在的整体灰暗,对比度低以及噪声等特点,利用改进的基于奇异值分解的增强算法,从而有效地增加图像对比度并抑制噪声。结果 为了验证本文算法的有效性,与带有色彩恢复的多尺度Retinex算法、GUM算法、Tarel算法、融合算法4种方法进行了对比。从增强结果可以看出,本文算法能够有效解决降质图像的颜色偏移和对比度低的问题,并增强图像的整体视觉效果。结论 本文算法充分利用沙尘降质图像三通道颜色直方图分布的特点,能够快速高效地实现颜色校正,并通过图像频域的奇异值信息进一步提升图像的对比度。经过多幅沙尘降质图像清晰化实验验证,表明本文方法能够实现对不同程度沙尘降质图像的增强,具有较强的适用性。     

基于最优模糊变换和Retinex理论的静脉图像增强方法

一种新的结合模糊变换和retinex理论静脉图像增强方法,可以解决近红外静脉图像所存在的低对比度,动态范围狭窄和强度分布不对称问题。最优模糊变换用于加强全局对比度,引入的Retinex方法可以增强图像细节信息,弥补最优模糊变换的细节缺失。由于图像从空间域向模糊域转换时使用一个参数优化隶属函数,处理的图像不具有最佳性,文中提出一种双参数的隶属函数的优化方法,同时提出一种自适应的选择控制参数方法。实验结果表明,该方法可以有效提高静脉图像与背景的对比度,与其他方法的实验结果相比较,可以看出该办法具有更好的图像增强性能。     

A fast minimization method for blur and multiplicative noise removal

Multiplicative noise and blur removal problems have attracted much attention in recent years. In this paper, we propose an efficient minimization method to recover images from input blurred and multiplicative noisy images. In the proposed algorithm, we make use of the logarithm to transform blurring and multiplicative noise problems into additive image degradation problems, and then employ l ₁-norm to measure in the data-fitting term and the total variation to measure the regularization term. The alternating direction method of multipliers (ADMM) is used to solve the corresponding minimization problem. In order to guarantee the convergence of the ADMM algorithm, we approximate the associated nonconvex domain of the minimization problem by a convex domain. Experimental results are given to demonstrate that the proposed algorithm performs better than the other existing methods in terms of speed and peak signal noise ratio.     

Computationally efficient algorithm for reducing the complexity of software radio receiver's filter bank

In this paper, a computationally efficient method for extracting individual radio channels from the output of the wideband analog to digital converter (ADC) is presented. In a software radio, the extraction of individual channels from the output of the wideband ADC is by far the most computationally demanding task; hence it is very important to devise computationally efficient algorithms for this task. We proposed a new algorithm by assuming the symmetric signal with periods of the length-P (number of coefficients in low pass filter prototype) as an input signal to the subsampled filter bank. Also we divide the complex input x[n] into real and imaginary parts, then we perform operations in each part using two parallel filter banks. Finally, we add the outputs in two parts. By employing this algorithm to the subsampled filter bank channelizer, the complexity of the proposed algorithm was reduced by considerable amount of 81%.     

Quantization error and dithering

Digital storage and transmission promise noise-free images, but it is important to keep in mind that even digital is not perfect. Digital images have their own sources of noise: round-off error and quantization error. Whenever you do any sort of image arithmetic, such as contrast enhancement or compositing, you get roundoff error. In fact, since the arithmetic is often done in only X-bit accuracy, sometimes the round-off error can be substantial. You get quantization error, on the other hand, whenever you go from an analog signal to a digital signal or whenever you go from a high color-resolution signal (for example, 24 bits per pixel) to a low resolution signal (for example, 8 bits per pixel). The author considers the quantization error from analog to digital     

基于人眼视觉特性的红外图像细节增强

由于红外成像的机理和探测器的限制,使得红外图像具有对比度低以及边缘模糊的特点。导致红外图像不适合被人眼及机器观察。传统的红外图像增强方法由于没有考虑人眼的因素,导致仍然不适合被人眼观察。本文结合分层思想提出一种基于人眼视觉的红外图像细节的增强的方法。该方法首先利用双边滤波器将图像分成包含图像低频概貌信息的基础层和包含图像高频细节信息的细节层。基础层的处理是运用人眼视觉特性的方法将其映射到显示器可显示范围内。细节层的处理是应用了自适应增益的方法对细节层进行增强。最后将两层的处理结果进行合并量化到8-bit范围内。采用大量的红外图像对算法进行测试,实验结果表明本算法能够改善红外图像对比度低和边缘模糊的缺点。     

Image Interpolation by Pixel-Level Data-Dependent Triangulation

We present a novel image interpolation algorithm. The algorithm can be used in arbitrary resolution enhancement, arbitrary rotation and other applications of still images in continuous space. High‐resolution images are interpolated from the pixel‐level data‐dependent triangulation of lower‐resolution images. It is simpler than other methods and is adaptable to a variety of image manipulations. Experimental results show that the new “mesh image” algorithm is as fast as the bilinear interpolation method. We assess the interpolated images' quality visually and also by the MSE measure which shows our method generates results comparable in quality to slower established methods. We also implement our method in graphics card hardware using OpenGL which leads to real‐time high‐quality image reconstruction. These features give it the potential to be used in gaming and image‐processing applications.     

基于FFT平移与相关分析的多尺度星载SAR自动配准

SAR图像配准是SAR图像应用,尤其是时间序列SAR图像应用的重要处理步骤之一。为实现重复星载SAR图像的快速、自动配准,通过将小波多尺度变换与快速傅立叶频谱变换相结合,实现了对星载SAR图像间初始偏移的快速估计,并在此基础上利用基于窗口的相关分析,实现了SAR图像的精确配准。选用星载ALOS-PALSAR和Radarsat-2影像作为试验数据,对提出的方法进行了实验分析。实验结果表明:该方法在无需任何先验知识的情况下,可以全自动完成重复轨道星载SAR数据的快速配准,且精度满足SAR干涉处理等时间序列SAR应用处理的需求,具有较强的鲁棒性。     

An effective local contrast enhancement technique by blending of local statistics and Genetic Algorithm

Nowadays, Image enhancement finds enormous image processing applications, which are related to practical situations, Contrast enhancement is one among the different image enhancement techniques that intends to improve the image visibility. Though several works for local contrast enhancement are available in the literature, the effectiveness remains an issue and the enhancement performance needs to be improved. In this paper, a local contrast enhancement technique is proposed for both gray scale images and RGB color images. The proposed technique is comprised of two stages of enhancement, namely, local statistics-based image enhancement and Genetic Algorithm based local contrast enhancement. The former stage is a pre-enhancement stage and the later is the major stage of enhancement. In the former stage, the image is processed in window basis and the local statistics of the image is obtained. Based on the local statistics, the image is enhanced. In the later stage, the window based operation is performed over the preenhanced image and the local contrast is enhanced. The Genetic Algorithm aids in searching of an optimal contrast factor, which plays vital role in the contrast enhancement. The technique is evaluated with both gray scale images as well as RGB color images and performance is compared with the existing contrast enhancement techniques.     

空间欠采样太赫兹时域光谱成像方法

文章针对传统太赫兹时域光谱成像技术存在的扫描时间长以及数据存储量大等问题,提出了一种基于压缩感知理论的空间欠采样太赫兹时域光谱成像方法。首先通过扫描电机获得目标非等间隔欠采样信号,然后利用压缩感知方法来重构缺失像素点的太赫兹信息。实验结果显示,当压缩比为0.5时,所重构的太赫兹信号与全采样条件下的信号相关性可达99.95%。通过对压缩重建图像的显示分析,时域图像中的缓变区域和频谱成像中的低频信号恢复效果较好。该方法为快速太赫兹光谱成像提供了一种有效的技术手段。     

基于直方图均衡化与MSRCR的沙尘降质图像增强算法

针对沙尘天气下图像色彩偏移严重及对比度低等问题,提出一种基于直方图均衡化与带色彩恢复的多尺度视网膜（MSRCR）增强的沙尘降质图像增强算法。通过偏色校正和图像增强两个步骤进行图像恢复,将RGB图像各通道预处理后利用限制对比度自适应直方图均衡方法得到校正后的图像,对图像采用双边滤波进行降噪处理,通过MSRCR算法进一步解决色彩失衡问题。由于处理后的图像对比度较低,存在一定色偏,利用伽马校正和基于图像分析的偏色检测及颜色校正方法进行处理得到最终结果。对大量沙尘降质图像进行仿真实验,结果表明,该算法能够有效处理不同偏色程度的沙尘图像,不仅提高了图像的对比度,而且有效避免了图像颜色偏移现象,相比GCANet、MSRCR等算法,平均时间效率提升了46.2%~94.7%。     

基于光能分配的遥感图像增强

遥感图像对比度的下降主要是由于光学系统调制传递甬数(MTF)的下降导致的,MTF反映了光学系统分配光能的特性.根据图像的对比度下降原理,提出了一种基于光能分配的遥感图像增强方法,其基本过程是首先将图像低频部分的光能进行抑制,然后通过Gamma校正将减少的低频部分的光能重新分配到高频部分,得到对比度提高的增强图像.实验结果表明,该方法实现简单,且取得了较好的增强效果,适用于遥感图像的增强.     

图像增强对显著性目标检测的影响研究

目的 雾霾、雨雪天气和水下等非理想环境因素会引起图像退化,导致出现低质图像,从而影响人类主观视觉感受及机器视觉应用任务的性能,因此,低质图像被利用之前进行图像增强成为惯常的预处理过程。然而,图像增强能否提高图像机器视觉应用任务的性能及影响程度等问题鲜有系统性研究。针对上述问题,本文以图像显著性目标检测这一机器视觉应用为例,研究图像增强对显著性目标检测性能的影响。方法 首先利用包括5种传统方法、6种深度学习方法等共11种典型图像增强方法对图像进行增强处理,然后利用8种典型的显著性目标检测方法对增强前后的图像分别进行显著性目标检测实验,并对比分析其结果。结果 实验表明,图像增强对低质图像显著性目标检测方法性能的促进作用不明显,某些增强方法甚至表现出负面影响,也存在同一增强方法对不同的显著性目标检测方法作用不同的现象。结论 图像增强对于显著性目标检测及其他的机器视觉应用的实际效果值得进一步研究,如何根据图像机器视觉应用的需求来选择和设计有效的增强方法需进一步探讨。     

基于新型模拟量信号的输出通道电路

在现代工业企业中,设备的自动化程度越来越高,自动控制系统在当代企业中已十分常见。而自动控制系统多数是对模拟量逬行控制,如物料的流量、压力等。为实现对这些模拟量的精确控制,现代自动控制系统多数采用闭环控制,这就需要对模拟量进行采集。然而,在工业设备应用中,为了使模拟量信号稳定、可靠的输出,需要对电压型输出环路检测短路故障,同时还需要对电流型输出检测开路故障的状况。本设计在这一方面极大提高了电子电路的运行状态及故障检测工作的精确性和检测效率,另一方面极大简化了电子电路检测设备的结构,可帮助增强系统总体可靠性,并在和利时自主研发的项目上得到成功应用。     

家装设计渲染图增强

目的 提出一种亮度、对比度、饱和度三要素与神经网络相结合的家装设计渲染图增强方法。方法 该方法分析了图像增强的3个要素：亮度、对比度和饱和度。算法从下列几个方面着手进行三要素的调节：1）根据原图饱和度和图像融合方法实现亮度和对比度增强;2）采用颜色矩阵实现饱和度增强;3）采用直方图均衡实现对比度进一步增强。这3个要素对图像增强的效果均有贡献,本文为三要素分别赋予一个权值,并引入神经网络方法,自动建立图像亮度分量均值、方差和饱和度分量均值、方差与三要素的权值系数的非线性映射关系。结果 根据图像本身的信息自动获取图像增强三要素的增强系数,实现家装设计渲染图的自适应增强。算法的有效性在不同程度偏灰暗的家装设计渲染图上得到了验证,并与几种经典方法进行了直方图、信息熵、平均对比度（AC）和平均灰度（AG）的定量比较。实验结果显示,本文算法实验结果的直方图具有很少的信息丢失和较好的特征保持,与遗传算法相比,信息熵提高了约0.2,AC值提高了约0.1,AG值提高了约15,本文算法在多数情况下评价指标优于改进的直方图方法。结论 通过对实验结果的直观评价与定量评价,证明与某些现有的方法相比,本文方法适用于不同程度偏灰暗的渲染图,具有较好的通用性,并能达到更优的渲染图像增强效果。