Fast image enhancement using multi-scale saliency extraction in infrared imagery

We propose a multi-scale saliency extraction based fast infrared image enhancement approach. A local frequency-tuned based saliency extraction technique is designed for highlighting the salient regions, firstly. Then, multi-scale saliency extraction is demonstrated, introducing multi-scale local windows with different sizes to extract regions of interest at different scales. Finally, the original image is enhanced with combining multi-scale salient image regions into one image. The experimental results prove the proposed approach is robust and efficient for infrared image enhancement.     

Infrared and visible image fusion method based on saliency detection in sparse domain

Infrared and visible image fusion is a key problem in the field of multi-sensor image fusion. To better preserve the significant information of the infrared and visible images in the final fused image, the saliency maps of the source images is introduced into the fusion procedure. Firstly, under the framework of the joint sparse representation (JSR) model, the global and local saliency maps of the source images are obtained based on sparse coefficients. Then, a saliency detection model is proposed, which combines the global and local saliency maps to generate an integrated saliency map. Finally, a weighted fusion algorithm based on the integrated saliency map is developed to achieve the fusion progress. The experimental results show that our method is superior to the state-of-the-art methods in terms of several universal quality evaluation indexes, as well as in the visual quality.     

一种结合小波分析与直方图的红外图像增强方法

针对传统红外图像增强算法中存在的问题,提出一种结合小波分析与直方图的红外图像增强方法.采用正交小波变换对红外图像进行处理,得到小波各层的分解系数;运用双向直方图均衡法对红外图像的低频子带小波系数进行处理,利用阈值滤波的细节系数增强法对红外图像的高频子带小波系数进行处理,经小波逆变换图像重构得到增强后的红外图像.实验结果...     

Infrared image enhancement using Cellular Automata

Image enhancement is a crucial technique for infrared images. The clear image details are important for improving the quality of infrared images in computer vision. In this paper, we propose a new enhancement method based on two priors via Cellular Automata. First, we directly learn the gradient distribution prior from the images via Cellular Automata. Second, considering the importance of image details, we propose a new gradient distribution error to encode the structure information via Cellular Automata. Finally, an iterative method is applied to remap the original image based on two priors, further improving the quality of enhanced image. Our method is simple in implementation, easy to understand, extensible to accommodate other vision tasks, and produces more accurate results. Experiments show that the proposed method performs better than other methods using qualitative and quantitative measures.     

基于灰度聚类算法的红外图像增强研究

根据红外灰度图像的特点，提出了一种基于K-均值聚类的图像增强的新算法。该算法首先根据具体图像确定K值，其次对红外图像的辐射温度数据进行统计学习，把不同温度值按升序排列，然后按等差原则选取温度值作为初始聚类中心，再依据初始聚类中心采用K-均值聚类算法对温度进行聚类，最后由聚类结果对图像进行自适应增强。通过对红外灰度图像进行实验，得到了满意的结果: 对比直方图均衡，具有更丰富的图像细节信息和层次感，视觉效果更好。     

Fingerprint image enhancement using multi-scale DDFB based diffusion filters and modified Hong filters

The main task of a fingerprint image enhancement is to enhance the image in such a way that it not only remove the noise but also enhance the reliable minutiae points. For this purpose, in this paper we propose a multi-scale decimation-free directional filter bank method for reliable orientation estimation. This reliable orientation is used in coherence enhancement diffusion and in Gabor filter based enhancement, which overcomes the drawbacks of these two methods. Experimental results show that the proposed method not only enhances the images but also facilitates the minutiae algorithm, by enhancing the true minutiae points.     

Novel infrared image enhancement technology based on the frequency compensation approach

A novel infrared image enhancement method has been proposed in this paper. Our aim is to develop a detail enhancement method which is focused on the frequency feature of the image. The proposed method is following the most popular strategy of enhancing the infrared images nowadays, but concentrating on the frequency domain. Firstly, the original image is separated by a guided image filter into detail layer and the base layer. Quite unlike the traditional methods, we use the guided image filter to eliminate most of the noise and weak signal of the scenario. Then, by a designed iteration process, the higher frequency of the scenario will be calculated back and add to the detail layer. The noise will not be enhanced because the iteration is only focused on the leftover scenario frequency. We run many tests on the raw data captured by the 320 × 256 HgCdTe cooled thermal imager, and make a comparison between our approach with the previous method of bilateral filtering digital detail enhancement and guided image filtering digital detail enhancement. Figures and analytical data show that our method is better than the previous proposed researches. Our method could effectively process the infrared image with less noise and artifacts, which has potential applications in testing, manufacturing, chemical imaging, night vision, and surveillance security.     

基于双边纹理滤波的图像细节增强方法

为了实现图像的细节增强,特别是纹理细节增强,同时尽可能保持图像的结构完整,提出了一种基于双边纹理滤波的图像多尺度分解方法。首先,对图像进行多尺度双边纹理滤波分解,分别得到一幅基本图像和一系列细节纹理图像。接着,类似于小波增强方法,对细节图像采用多尺度自适应增强方法,得到一系列增强后的纹理细节图像。最后,将基本图像和增强后细节图像相加,重构出最后的增强图像。实验结果表明:本文提出的增强方法能够在突出边缘的同时,较好地增强图像中的纹理细节信息。将基于双边纹理滤波的多尺度分解引入图像增强,能更好地体现图像纹理细节特征,为增强图像提供更加丰富的信息。     

Infrared and visible image fusion based on visual saliency map and weighted least square optimization

The goal of infrared (IR) and visible image fusion is to produce a more informative image for human observation or some other computer vision tasks. In this paper, we propose a novel multi-scale fusion method based on visual saliency map (VSM) and weighted least square (WLS) optimization, aiming to overcome some common deficiencies of conventional methods. Firstly, we introduce a multi-scale decomposition (MSD) using the rolling guidance filter (RGF) and Gaussian filter to decompose input images into base and detail layers. Compared with conventional MSDs, this MSD can achieve the unique property of preserving the information of specific scales and reducing halos near edges. Secondly, we argue that the base layers obtained by most MSDs would contain a certain amount of residual low-frequency information, which is important for controlling the contrast and overall visual appearance of the fused image, and the conventional “averaging” fusion scheme is unable to achieve desired effects. To address this problem, an improved VSM-based technique is proposed to fuse the base layers. Lastly, a novel WLS optimization scheme is proposed to fuse the detail layers. This optimization aims to transfer more visual details and less irrelevant IR details or noise into the fused image. As a result, the fused image details would appear more naturally and be suitable for human visual perception. Experimental results demonstrate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.     

Infrared moving object detection based on local saliency and sparse representation

The key issue of infrared object detection is to locate moving object in image sequence. In order to improve detection precision, an infrared object detection method based on local saliency and sparse representation is proposed in this paper. Motion information, such as velocity, acceleration components are added into the eigenvectors to build local saliency model. And the approximate position of the infrared target is located based on the local saliency. To accurately extract the infrared object, sparse representation is used to capture complete edge of the object. Experiments show that the proposed method can accurately detect infrared moving objects, and has good robustness to external disturbances and dynamic background.     

Blood vessel enhancement for DSA images based on adaptive multi-scale filtering

Digital subtraction angiography (DSA) plays a significant role in the diagnosis, treatment planning and assessment of diseases. However, because of the geometrical complexity and fine characteristics of blood vessel structures, accurate and robust detection of blood vessels still remains a problem. In this paper, a blood vessel enhancement algorithm is proposed. The main purpose of this work is to improve the visual quality of blood vessels in DSA images. The new blood vessel enhancement algorithm is based on the multi-scale space theory and Hessian matrix. Not only the eigenvalues of Hessian matrix but also the angles between eigenvectors are utilized for the blood vessel enhancement of DSA. The filter parameters and scale factors are decided adaptively. Eigenvalues of the Hessian matrix are also used for the noise elimination. Experimental results show that the proposed algorithm has a good performance in blood vessel enhancement of DSA images. The proposed algorithm filters image background and non-vascular structure effectively. The deformation of blood vessels occurred in the enhancement process is avoided and more small blood vessels are visible in DSA images.     

Infrared small moving target detection using sparse representation-based image decomposition

Infrared small moving target detection is one of the crucial techniques in infrared search and tracking systems. This paper presents a novel small moving target detection method for infrared image sequence with complicated background. The key points are given as follows: (1) since target detection mainly depends on the incoherence between target and background, the proposed method separate the target from the background according to the morphological feature diversity between target and background; (2) considering the continuity of target motion in time domain, the target trajectory is extracted by the RX filter in random projection. The experiments on various clutter background sequences have validated the detection capability of the proposed method. The experimental results show that the proposed method can robustly provide a higher detection probability and a lower false alarm rate than baseline methods.     

基于特征融合的红外图像增强算法

在红外图像处理过程中,提高图像的对比度,抑制噪声以及突出图像细节尤为重要,针对这些问题,提出了基于特征融合的红外图像增强算法.以引导滤波为基础对图像进行平滑分层,得到基本层和细节层,基本层采用CLA-HE算法扩展低频分量的范围,而对于细节层采用Log算子与Laplace算子分别处理,依据梯度因子的权重信息获得融合细节层...     

Defects’ geometric feature recognition based on infrared image edge detection

Edge detection is an important technology in image segmentation, feature extraction and other digital image processing areas. Boundary contains a wealth of information in the image, so to extract defects’ edges in infrared images effectively enables the identification of defects’ geometric features. This paper analyzed the detection effect of classic edge detection operators, and proposed fuzzy C-means (FCM) clustering-Canny operator algorithm to achieve defects’ edges in the infrared images. Results show that the proposed algorithm has better effect than the classic edge detection operators, which can identify the defects’ geometric feature much more completely and clearly. The defects’ diameters have been calculated based on the image edge detection results.     

Two-scale image fusion of visible and infrared images using saliency detection

Military, navigation and concealed weapon detection need different imaging modalities such as visible and infrared to monitor a targeted scene. These modalities provide complementary information. For better situation awareness, complementary information of these images has to be integrated into a single image. Image fusion is the process of integrating complementary source information into a composite image. In this paper, we propose a new image fusion method based on saliency detection and two-scale image decomposition. This method is beneficial because the visual saliency extraction process introduced in this paper can highlight the saliency information of source images very well. A new weight map construction process based on visual saliency is proposed. This process is able to integrate the visually significant information of source images into the fused image. In contrast to most of the multi-scale image fusion techniques, proposed technique uses only two-scale image decomposition. So it is fast and efficient. Our method is tested on several image pairs and is evaluated qualitatively by visual inspection and quantitatively using objective fusion metrics. Outcomes of the proposed method are compared with the state-of-art multi-scale fusion techniques. Results reveal that the proposed method performance is comparable or superior to the existing methods.     

Infrared/laser multi-sensor fusion and tracking based on the multi-scale model

The state estimation problem of targets detected by infrared/laser composite detection system with different sampling rates was studied in this paper. An effective state estimation algorithm based on data fusion is presented. Because sampling rate of infrared detection system is much higher than that of the laser detection system, the theory of multi-scale analysis is used to establish multi-scale model in this algorithm. At the fine scale, angle information provided by infrared detection system is used to estimate the target state through the unscented Kalman filter. It makes full use of the high frequency characteristic of infrared detection system to improve target state estimation accuracy. At the coarse scale, due to the sampling ratio of infrared and laser detection systems is an integer multiple, the angle information can be fused directly with the distance information of laser detection system to determine the target location. The fused information is served as observation, while the converted measurement Kalman filter (CMKF) is used to estimate the target state, which greatly reduces the complexity of filtering process and gets the optimal fusion estimation. The simulation results of tracking a target in 3-D space by infrared and laser detection systems demonstrate that the proposed algorithm in this paper is efficient and can obtain better performance than traditional algorithm.     

奇异值非线性修正的低对比度红外图像实时增强

由于场景中目标与背景的温差相对较小,红外图像会存在对比度低、视觉效果差的问题,针对这一问题,提出一种基于奇异值非线性修正的红外图像对比度实时增强方法。该方法首先对红外图像进行奇异值分解得到其原始奇异值,然后采用一个对数型非线性变换对图像奇异值进行优化,最后根据修正的奇异值重构出对比度增强的红外图像。利用对数型非线性变换修正图像奇异值不仅能够有效拉伸奇异值的动态范围,同时可优化奇异值的变化梯度,使图像的能量信息得到更充分地表达,改善红外图像不良的视觉效果。实验结果表明,该方法较几种对比方法在视觉效果和客观评价方面均具有更优的增强性能;同时体现出良好的实时性,为实现红外图像的实时增强提供了新途径。     

奇异值非线性修正的低对比度红外图像实时增强

由于场景中目标与背景的温差相对较小,红外图像会存在对比度低、视觉效果差的问题,针对这一问题,提出一种基于奇异值非线性修正的红外图像对比度实时增强方法。该方法首先对红外图像进行奇异值分解得到其原始奇异值,然后采用一个对数型非线性变换对图像奇异值进行优化,最后根据修正的奇异值重构出对比度增强的红外图像。利用对数型非线性变换修正图像奇异值不仅能够有效拉伸奇异值的动态范围,同时可优化奇异值的变化梯度,使图像的能量信息得到更充分地表达,改善红外图像不良的视觉效果。实验结果表明,该方法较几种对比方法在视觉效果和客观评价方面均具有更优的增强性能;同时体现出良好的实时性,为实现红外图像的实时增强提供了新途径。