Image synthesizing for all-weather outdoor scenes

Lighting simulation and image synthesis for outdoor scenes are difficult because of the geometric complexity and the presence of lighting interaction and atmospheric extinction in the outdoor environment. An efficient method of image synthesis by use of a linear combination of basic images is presented. With respect to linearity of the illumination function, the coefficients of basic images of various Sun positions and sky situations can be calculated. Furthermore, the relationship among visibility distance, image contrast, and resolution is addressed so that the desired images can be generated under all weather conditions. It is shown that a uniform formula is available and feasible for fixed scene and camera geometry.     

基于改进暗通道算法的图像去雾研究

为了提高图像去雾效果,提出一种改进暗通道(IDCP)算法,通过缩小图像结合3个暗通道线性拟合方法对透射率进行精确计算,在大气值一定区间内利用四叉树细分算法求取图像的大气光值最终估计值,为避免图像饱和度偏低进行了颜色补偿。实验仿真显示该算法去雾后的图像在视觉上更加清新自然,对不同的图像的客观评价值优于其他算法。     

Single image dehazing using multiple transmission layer fusion

Methods for single image dehazing have been widely studied based on the atmospheric scattering model and dark channel prior (DCP); they usually adopt an additional refinement procedure such as guide filtering to restrain the halo artefacts, but it easily induces undesirable textures in the final transmission map, and further leads to an overall contrast reduction and detail blur. In this paper, an efficient approach was proposed to enhance single hazy images without any refined post-process, which is based on the strategy of multiple transmission layers fusion. In order to estimate the final transmission map adapting to different scenes reasonably, the multiple transmission layers were derived based on DCP with different kinds of adaptive local watch windows. To make sure the atmospheric light is estimated in the most haze-opaque region, the corresponding region was searched hierarchically with the quadtree subdivision method in the top part of the minimal channel of the input image. Finally, the hazy image was restored through solving the scattering model. Comparison experiments verify that the proposed method is straightforward and efficient, which can reduce the halo artefacts significantly, yielding satisfactory contrast and colour for varied hazy images.     

Improvement of face recognition using light compensation technique on real-time imaging

Camera imaging systems are used widely. However, the resulting images may show unequal light distributions due to backlight. In this paper, an adaptive backlight compensation algorithm is presented for fixing the brightness and contrast in regions of interest, particularly for human faces. The framework is implemented in two stages. The first stage is the light compensation algorithm, which depends on face detection and focuses on the intensities of pixels in ‘face’ regions only. The second stage uses a distance weighting approach to address artificial light effects created by the first stage. This algorithm can adjust the imaging light distribution adaptively in order to solve the problem of backlight and achieve natural-looking pictures. For face recognition systems, this approach can improve the success rate for face recognition by 35% on average when images are backlit.     

A New Medical Image Enhancement Algorithm Based on Fractional Calculus

The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might influence the accuracy of the diagnosis process. To overcome this problem, this paper presents a new fractional integral entropy (FITE) that estimates the unforeseeable probabilities of image pixels, posing as the main contribution of the paper. The proposed model dynamically enhances the image based on the image contents. The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’ probability. Initially, the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image. Next, the contrast of the image is then adjusted to enhance the regions with low visibility. Finally, the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image. Tests were conducted on brain MRI, lungs CT, and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality. The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores. Overall, this model improves the details of brain MRI, lungs CT, and kidney MRI scans, and could therefore potentially help the medical staff during the diagnosis process.     

Visibility Enhancement of Scene Images Degraded by Foggy Weather Condition: An Application to Video Surveillance

In recent years, video surveillance application played a significant role in our daily lives. Images taken during foggy and haze weather conditions for video surveillance application lose their authenticity and hence reduces the visibility. The reason behind visibility enhancement of foggy and haze images is to help numerous computer and machine vision applications such as satellite imagery, object detection, target killing, and surveillance. To remove fog and enhance visibility, a number of visibility enhancement algorithms and methods have been proposed in the past. However, these techniques suffer from several limitations that place strong obstacles to the real world outdoor computer vision applications. The existing techniques do not perform well when images contain heavy fog, large white region and strong atmospheric light. This research work proposed a new framework to defog and dehaze the image in order to enhance the visibility of foggy and haze images. The proposed framework is based on a Conditional generative adversarial network (CGAN) with two networks; generator and discriminator, each having distinct properties. The generator network generates fog-free images from foggy images and discriminator network distinguishes between the restored image and the original fog-free image. Experiments are conducted on FRIDA dataset and haze images. To assess the performance of the proposed method on fog dataset, we use PSNR and SSIM, and for Haze dataset use e, r⁻, and σ as performance metrics. Experimental results shows that the proposed method achieved higher values of PSNR and SSIM which is 18.23, 0.823 and lower values produced by the compared method which are 13.94, 0.791 and so on. Experimental results demonstrated that the proposed framework Has removed fog and enhanced the visibility of foggy and hazy images.     

An Optimum Shift-and-Weighted brightness mapping for low-illumination image restoration

Images captured under low-illumination environments often impose difficulties in revealing objects of interest. An effective approach, Optimum Shift-and-Weighted Brightness Mapping, is here proposed that can optimally enhance the image for higher brightness, information content, and colour vividness. Specifically, the input-output brightness mapping is determined by a shifted spline curve and a larger amplification is allowed for low-brightness pixels. A weighting function is further applied such that high brightness pixels are preserved. The final enhanced image is obtained by inserting the extracted high frequency components from the original input to the brightness boosted image. The algorithm is adaptive to image contents where parameters are optimized using the efficient golden section search instead of relying on user specified coefficients. Experimental results, from a large set of test images, showed that better quality images could be obtained on a variety of low-illumination scenarios as compared to several recent approaches.     

一种新的ICA域图像融合算法

针对红外和可见光图像的特点,结合Mitianoudis提出的ICA域图像融合方法,本文提出了一种改进的ICA域多模图像融合算法.该方法根据Mitianoudis的方法,通过训练得到的基函数对图像进行线性变换,在变换域中将图像分割成不同的区域,对活跃区域采用绝对值取大的融合规则,而对非活跃区域则按照目标传感器图像的区域分割结果分别采取不同的融合规则,最后反变换得到融合图像.实验结果表明了本丈方法的有效性.     

Very-wide-angle optical systems suitable for spaceborne photometric measurements

Spaceborne visible-light images for observing the large angular extent of the solar corona require 0.1% differential broadband photometry over ~1 degrees sky bins. When we are using a CCD camera, this specification requires spreading unresolved images over many pixels. Large images ease correction for aberration or field curvature. Permitting large images allows simple and lightweight very-wide-angle designs employing spherical and toroidal mirrors and thick lenses that can view almost the entire sky. We present formulas and graphic results relating sky angle to focal-plane position and determining the tangential and sagittal focal surfaces governing image size at the CCD. Laboratory measurements with two prototype configurations confirm the calculations.     

一种改进的自然纹理合成方法

自然纹理合成方法是一种适合自然景物的基于样图的快速纹理合成方法。但是候选点超越样图边界的问题没有很好得到解决,成为导致合成后图像产生的纹理块间明显变化的主要因素。论文提出了一种改进的自然纹理合成算法,将样图边缘易产生无效候选点的区域用样图内部与之大小和形状相同的像素块来代替,像素块和被替代像素块沿一条不规则的曲线相匹配。合成过程中在接近边缘时像素块的生长会转向纹理内部。该方法减少了因随机产生候选点而形成的块间不连续,有效地改善了视觉效果。     

Single sand-dust image restoration using information loss constraint

Abstract

Outdoor images captured during sand–dust weather condition typically yield poor contrast and colour shift. A novel method for single sand–dust images restoration is introduced in this paper, which relies on the atmospheric scattering model and information loss constraint. To compensate the colour shift and achieve proper luminance, the proposed atmospheric light is changing with the content of the local scenes, which is initially estimated on the basis of the general scattering model and the grey-world assumption. Then, the initial atmospheric light is updated and the coarse transmission is estimated under the information loss constraint. Next, the fast guide filter is exploited in the post-refinement process to inhibit the halo artefacts. Comparison experiments demonstrate that the proposed algorithm is straightforward and efficient, the contrast and colour shift of different kinds of sand-dust images can be well compensated, especially, nice colour fidelity and proper luminance can be maintained.     

Noise-suppressed image enhancement using multiscale top-hat selection transform through region extraction

Enhancing an image through increasing the contrast of the image is one effective way of image enhancement. To well enhance an image and suppress the produced noises in the resulting image, a multiscale top-hat selection transform-based algorithm through extracting bright and dark image regions and increasing the contrast between them is proposed. First, the multiscale top-hat selection transform is discussed and then is used to extract the bright and dark image regions of each scale. Second, the final extracted bright and dark image regions are obtained through a maximum operation on all the extracted multiscale bright and dark image regions at all scales. Finally, by using a weight strategy, the image is enhanced through increasing the contrast of the image by adding the final bright regions on and subtracting the final dark regions from the original image. The weight parameters are used to adjust the effect of image enhancement. Because the multiscale top-hat selection transform is used to effectively extract the final image regions and discriminate the possible noise regions, the image is well enhanced and some noises are suppressed. Experimental results on different types of images show that our algorithm performs well for noise-suppressed image enhancement and is useful for different applications.     

拓扑梯度耦合FCMC的全自动图像修复优化算法

目的当前图像修复算法的损坏区域大都是依靠人工来确定,难以自动鉴定损坏区域,使其修复效率较低。此类算法通过利用像素缺失区域的间断边缘来完成填充,导致重构图像视觉间断,且都是依赖随机修复路径,增加了算法时耗。提出拓扑梯度最小重构路径耦合FCMC(Fuzzy C-mean Clustering)的全自动图像修复算法。方法基于图像损坏区域与完好区域之间的性质差异,引入模糊C均值(FCMC),通过损坏区域的聚类中心与各像素之间的距离来计算隶属度函数,设计基于FCMC的损坏区域自动鉴定算法,以自动识别待修复区域;再嵌入拓扑梯度,定义像素缺失区域的关键点选择规则,建立权重距离函数,得到像素缺失区域的连续轮廓,设计最低修复路径成本方案,完成图像重构;以PSNR(Peak Signal to Noise Ratio)为评估指标,构造图像修复反馈机制,优化修复图像。结果仿真结果显示:与当前图像修复算法相比,该算法可自动鉴定图像像素缺失区域,能够提取像素缺失区域的连续轮廓。同时,具有更好的修复视觉效果与更高的修复效率,重构图像不存在模糊与视觉不连通。结论提出的算法能够实现图像的全自动修复,可提高修复图像质量与效率。     

Temperature Dependence of Fixed Pattern Noise in Logarithmic CMOS Image Sensors

This paper presents a model that is then simplified to explain the temperature dependence of fixed pattern noise (FPN) in logarithmic complementary metal–oxide semiconductor (CMOS) image sensors. The simplified model uses the average dark response of pixels, which depends only on temperature, to help predict the FPN in the light response, which depends on temperature and illuminance. To calibrate a logarithmic camera, one requires images that are taken at different temperatures and illuminances, which need not be measured, of a uniform stimulus. To correct the FPN in an arbitrary image, one uses the simplified model parameters, which are estimated once by the calibration, and the average dark response, which is infrequently determined by closing the aperture. Through simulation (using mismatch data from a real CMOS process) and experiment (using a commercial logarithmic camera), an improvement is shown in the residual error per image, after calibration, when the proposed method is compared with a related method in the literature that does not account for temperature dependence.      

Estimation of saturated pixel values in digital color imaging

Pixel saturation, in which the incident light at a pixel causes one of the color channels of the camera sensor to respond at its maximum value, can produce undesirable artifacts in digital color images. We present a Bayesian algorithm that estimates what the saturated channel's value would have been in the absence of saturation. The algorithm uses the nonsaturated responses from the other color channels, together with a multivariate normal prior that captures the correlation in response across color channels. The prior may be estimated directly from the image data, since most image pixels are not saturated. Given the prior and the responses of the nonsaturated channels, the algorithm returns the optimal expected mean square estimate for the true response. Extensions of the algorithm to the case in which more than one channel is saturated are also discussed. Both simulations and examples with real images are presented to show that the algorithm is effective.     

基于局部估计的多光源图像颜色恒常性计算

目的解决多光源场景图像的颜色矫正问题。方法首先采用网格划分和关键点取样2种方法对多光源图像划分区域,然后对划分后的区域采用单光源颜色恒常性算法估计光源,把每一个区域对场景光源颜色的贡献整合为复合光源颜色作为多光源场景的近似估计,最后采用对角模型进行矫正,并将此方法与单独用单光源颜色恒常性算法估计做对比。结果通过对多光源图像划分区域,可以弱化多个光源对图像的影响,与单独用单光源颜色恒常性算法估计对比,对图像的矫正效果显著。结论通过局部估计多光源图像,所提出的方法可以有效解决多光源室外场景图像的颜色恒常性问题。     

WISECODE: wise image segmentation based on community detection

Image segmentation is one of the fundamental problems in image processing and computer vision, since it is the first step in many image analysis systems. This paper presents a new perspective to image segmentation, namely, segmenting input images by applying efficient community detection algorithms common in social and complex networks. First, a common segmentation algorithm is used to fragment the image into small initial regions. A weighted network is then constructed. Each initial region is mapped to a vertex, and all these vertices are connected to each other. The similarity between two regions is calculated from colour information. This similarity is then used to assign weights to the edges. Afterwards, a community detection algorithm is applied, and communities are extracted such that the highest modularity measure is achieved. Finally, a post-processing algorithm merges very small regions with the greater ones, further enhancing the final result. One of the most striking features of the proposed method, is the ability to segment the input image without the need to specify a predefined number of segments manually. This remarkable feature results from the optimal modularity value, which is utilised by this method. It is also able to segment the input image into a user defined number of segments. Extensive experiments have been performed, and the results show that the proposed scheme can reliably segment the input colour image into good subjective criteria.     

Hiding a secret colour image in two colour images

Abstract

Visual cryptography is different from traditional cryptography. That is, neither time-consuming computation nor complex cryptographic knowledge is needed. Stacking is the only operation required to recover a secret image, and the individual image does not give the hackers any information about the secret image. None of researches tried to deal with meaningful colour share transparencies. Hence, two methods are proposed for hiding a colour image in two meaningful colour share transparencies in this paper. To achieve this goal, the colour decomposition approach and halftone technology are first applied to cope with secret colour images. Then the concept of the human visual system is utilized to generate two colour meaningful sharing transparencies. To support various applications, two variants are presented. The first proposed method, method-1, is suitable for simple colour images, and the second, method-2, provides better visibility of complex colour images.     

An Efficient Image Analysis Framework for the Classification of Glioma Brain Images Using CNN Approach

The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours. The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work. The non-active cells in brain region are known to be benign and they will never cause the death of the patient. These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels. The Magnetic Resonance (MR) image contrast is improved by the cost map construction technique. The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method. This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features. Using k-mean clustering algorithm the tumor regions in glioma are classified. The proposed algorithm provides high sensitivity, specificity and tumor segmentation accuracy.