基于FPGA的视频实时去雾算法及其硬件实现

单幅图像去雾技术虽然已经取得较大的进展,但是算法较为复杂,运行时间较长.为了实现视频实时去雾,以硬件实现为目的,对暗通道先验算法进行改进,降低其时间复杂度.提出了一种暗通道图优化方法,保留了图像的边缘信息,消除了光晕效应,省去了透射率细化的复杂操作;提出了适应于硬件实现的大气光值估计和调节及透射率补偿方法,解决了视频帧间闪烁及天空等明亮区域的色彩失真问题.基于现场可编程门阵列(FPGA)对所提出算法进行了硬件实现.结果表明,该算法可以实时处理帧速为60 f/s、分辨率为1 920×1 080的视频图像,相比传统去雾算法速度更快,去雾质量更高.     

一种改进实时图像去雾的暗原色先验算法

针对暗原色先验图像去雾算法运行时间长,天空区域分割耗时,复原图像中存在方块效应以及整体偏暗等缺点,提出一种改进的实时图像去雾暗原色先验算法。在暗通道求取时,使用快速最小值滤波,加快传统暗通道求取最小值排序的时间;在大气光求取时,使用像素值限定的方法快速排除天空区域,使用暗通道图像剩余最亮部分对应原图像像素值的均值,有效避免原算法中求取大气光值过大导致复原图像失真的现象;在透射率的优化时,使用Sobel边缘检测与求取图像二阶导数图像,得到精细的透射率,改善了细化透射率的时间复杂度;对最终图像采取伽码变换,提高图像亮度。实验结果表明,与原暗通道先验算法相比,此算法有效提高了算法的实时性。     

基于暗原色和加权形态学滤波的图像去雾算法

针对雾天图像能见度低、对比度差的特点,提出一种自动消除雾的方法：基于暗原色和加权形态滤波的增强算法。首先引入暗原色先验信息,然后利用形态学滤波方法估计雾浓度图。该方法既能平滑雾浓度图,又能很好地保留场景的边缘,使估计出的雾浓度图更加精确。最后恢复去雾图像。实验结果表明,该方法简单快速有效,能够很好地达到去雾目的,并且较好地保留图像边缘细节。     

结合暗通道先验和MRF理论的单幅图像去雾算法

针对在雾霾环境下获取的图像降质严重、现有算法去雾图结构细节信息丢失较多的问题,提出了一种结合暗通道先验(DCP)和马尔可夫随机场(MRF)的单幅图像去雾算法。该算法先采用子块部分重叠局部直方图均衡(POSHE)对原始雾图进行增强,以提高其对比度,并通过DCP算法获取优化后的透射率;利用MRF模型对图像结构细节信息的约束特性,对透射率进行建模,以进一步细化透射率;由天空域的显著特征,通过分块搜索法求取大气光值。与传统去雾算法相比,该算法能得到更精确的透射率图,有效保持图像结构信息,去雾后的图呈现出丰富的细节和较真实的色彩视觉效果。     

基于暗原色先验去雾的改进算法

暗原色先验去雾方法在单幅图像去雾方面效果明显,但该方法算法复杂度高、处理耗时。针对该算法不足之处,在传统的暗原色先验去雾方法基础上,提出一种改进算法。改进算法通过高斯滤波和腐蚀对透射率图进行优化;同时,为避免原算法的冗余计算,采用了一种快速计算初始透射率方法;并且通过Gamma变换和对比度增强方法对去雾后图像进行亮度和对比度增强处理。实验结果显示,改进算法处理效果与原算法基本一致,算法效率得到显著提高,应用在视频增强领域可以达到准实时。     

基于非局部先验的雾线优化图像去雾算法

针对非局部先验去雾算法中雾线端点像素位置精确度不足的问题,提出了雾线优化的非局部先验图像去雾算法。首先分析雾线理论,结合暗通道理论确定最大聚类雾线真实端点,以其为已知条件补偿小聚类雾线端点与大气光之间的距离,根据类内不同像素与雾线对应夹角预估单个像素雾线端点进而求得像素级优化后的透射率,最后根据图像局部灰度值差异融合暗通道先验(dark channel prior, DCP)和非局部先验透射率得最终透射率图。将本文算法与其余3种去雾算法在多幅户外雾图下通过主观及客观两方面分析比较,实验结果表明该算法能取得更好的去雾效果,尤其在天空区域图像复原效果较为突出。     

一种基于暗通道的航拍图像去雾算法

针对无人机航拍图像在雾霾天气下降质特点,提出一种基于暗通道先验原理的航拍图像去雾改进算法。根据大气物理模型和暗通道原理,对有雾图像进行自适应分块求取暗通道图;通过降采样得到初始透射率图,再应用导引滤波对投射率进行优化,同时利用最小值滤波和均值滤波求解大气光强;应用雾天模型恢复降质图像。通过实验对比验证,该算法简单且能够高效去雾,在客观评价指标上总体性能也优于其他算法。     

改进的雾线先验透射率优化图像去雾算法

针对传统去雾算法处理图像后存在颜色不均衡、能见度较低等问题,提出一种基于雾线先验的双边滤波优化透射率算法。首先,将像素在RGB空间中聚类成雾线并引入自适应模块对大气光值进行预估。将大气散射模型和上下文正则化原理相结合,对图像透射率进行初步优化,同时基于最小通道对透射率进行更正,使得传输率图更加平滑,防止相邻景深区域透射率差距过大;再经过双边滤波对透射率进行二次优化,使其变得更加精准。然后,将大气光值和透射率输入到大气散射模型进行去雾处理得到无雾图像。最后,将去雾后的图像进行色彩增强,以提升图像的色彩真实性和亮度。实验结果表明,所提方法在主观上提升了人眼的视觉效果,在客观评价指标结构相似性(SSIM)、峰值信噪比(PSNR)、角点检测数、通用质量指数(UQI)、自然图像质量评估(NIQE)和处理时长上均有着显著的优越性。     

一种基于降维滤波的快速去雾算法

为了达到单幅图像的快速去雾,提出了一种基于降维滤波的快速去雾算法.采用边界约束来求取透射率图,然后用降维滤波对透射率图进行优化,减少了计算量;通过自适应调节大气光值和透射率下限值优化了天空区域的失真;对去雾后的图像进行自适应Zadeh-X变换改善图像的视觉效果.通过对多种带雾图像实验表明该算法可以达到高速和高质量的去雾,并且适用于偏暗、偏亮和颜色不丰富的带雾场景.     

基于深度卷积神经网络的自适应图像去雾算法

室外拍摄图像由于受雾气、雾霾、沙尘等大气颗粒杂质的影响呈现出图像灰白化,而现有的图像去雾算法存在过度依赖先验信息、透射率计算不精确的问题。针对上述问题,文中提出了一种基于深度卷积神经网络的自适应图像去雾算法。该算法基于大气散射模型实现了有雾图像的去雾,设计浅层提取、并行提取和深度融合共3个全卷积网络实现图像浅层特征与深层特征的融合,大幅提高了透射率图的准确性。去雾实验测试结果表明,文中所提出的算法对室外露天雾图具有良好的去雾效果,且去雾细节效果更加理想。     

基于带权值无向图的视频隐写分析

针对视频帧的时域特性,提出一种以帧间相关性构建带权值无向图的视频隐写分析方法。首先,分别计算待检测视频每帧亮度的灰度共生矩阵,把得出的8维特征作为该帧的特征向量。再以视频帧为节点,用帧间特征向量的欧氏距离作为节点间的权值,构造出表示帧间相关性的带权值无向图。根据嵌入信息后帧间相关性发生改变的特性来判断视频中是否有秘密信息嵌入。实验结果表明,用带权值无向图的方法可以快速准确地区分载密视频和原始视频,并且有较高的正确率。     

Underwater video dehazing based on spatial–temporal information fusion

In this paper, a novel multidimensional underwater video dehazing method is presented to restore and enhance the underwater degraded videos. Videos in the underwater suffer from medium scattering and light absorption. The absorption of light traveling in the water makes the underwater hazing videos different from the atmosphere hazing videos. In order to dehaze the underwater videos, a spatial–temporal information fusion method is proposed which includes two main parts. One is transmission estimation, which is based on the correlation between the adjacent frames of videos to keep the color consistency, where fast tracking and the least square method are used to reduce the influence of camera and object motions and water flowing. Another part is background light estimation to keep consistent atmospheric light values in a video. Extensive experimental results demonstrate that the proposed algorithm can have superior haze removing and color balancing capabilities.     

GOP-Level Dynamic Thermal Management in MPEG-2 Decoding

In this paper, we present a dynamic thermal management (DTM) algorithm based on: 1) accurate estimation of the workload of frames in a group of pictures (GOP) in an MPEG-2 video stream and 2) slack borrowing across the GOP frames in order to achieve a thermally safe state of operation in microprocessors during the video decoding process. The proposed DTM algorithm employs dynamic voltage and frequency scaling (DVFS) while considering the frame-rate-dependent GOP deadline, variance of the frame decoding times within the GOP, and a maximum chip temperature constraint. If it becomes necessary to sacrifice video quality or violate the GOP deadline due to a low temperature bound, then the (intra-frame) spatial quality degradation and the (inter-frame) temporal quality degradation will be applied to the GOP. Experimental results demonstrate the competence and efficiency of the proposed online DTM algorithm.      

Aethra-net: Single image and video dehazing using autoencoder

A fast and efficient video dehazing system with low computational complexity has a huge demand among drivers during hazy winter nights. There are only a few video dehazing models that exist in literature. Video dehazing requires the sequential extraction and processing of frames. The processed frames must be restored in the same sequence as the original video. However, the existing video dehazing algorithms suffer from color distortion due to the continuous processing of frames. They are not suitable for videos with dense haze. Furthermore, some dehazing systems require hardware, whereas the proposed model is completely software-based to reduce the computational costs. In this paper, an image and video dehazing system called Aethra-Net is developed. A gush enhancer-based autoencoder is modified to obtain the transmission map. The structure of gush enhancement module resembles the processing of light entering the human eye from different paths. The multiple blocks of Resnet-101 layers are employed to overcome vanishing gradient problem. The vessel enhancement filter is also incorporated to enhance the performance of the proposed system. The proposed model has a susceptibility to compute the dehazed images effectively. The proposed model is evaluated on various benchmark datasets and compared with the existing dehazing techniques. Experimental results reveal that the performance of Aethra-Net is found superior as compared to the existing dehazing models.     

基于三角形循环二分算法的图像去雾研究

为了提高图像去雾效果,采用三角形循环二分算法处理图像。首先分析基本暗通道算法图像去雾;接着基于三角形循环二分算法对大气光值精确计算,将图像沿对角线分成4个三角形区域,只对雾浓度最较高的区域进行二分图形划分,选取三角形最长边的中点,连接到对应三角形最长边的顶点,循环执行直到满足预先设定的面积阈值;然后自适应调节透射率,避免色彩失真;最后给出了算法流程。实验仿真显示,本文算法去雾后的图像在视觉上更加清晰,可见边集合数目比、平均梯度比值、直方图相似度系数、综合评价指标相比其他算法较好,能够满足图像去雾对清晰度、信息量的要求。     

Improvement of color video demosaicking in temporal domain.

Color demosaicking is critical to the image quality of digital still and video cameras that use a single-sensor array. Limited by the mosaic sampling pattern of the color filter array (CFA), color artifacts may occur in a demosaicked image in areas of high-frequency and/or sharp color transition structures. However, a color digital video camera captures a sequence of mosaic images and the temporal dimension of the color signals provides a rich source of information about the scene via camera and object motions. This paper proposes an inter-frame demosaicking approach to take advantage of all three forms of pixel correlations: spatial, spectral, and temporal. By motion estimation and statistical data fusion between adjacent mosaic frames, the new approach can remove much of the color artifacts that survive intra-frame demosaicking and also improve tone reproduction accuracy. Empirical results show that the proposed inter-frame demosaicking approach consistently outperforms its intra-frame counterparts both in peak signal-to-noise measure and subjective visual quality.     

基于暗原色优化算法的去雾研究

针对基于传统的暗原色先验去雾算法中,由于某些场景下的雾天图像存在大面积明亮区域无法满足暗原色先验的假设,导致去雾效果不佳。文中就此问题提出了一种改进的去雾算法,基于McCartnet的理论建立大气散射模型,根据暗通道理论粗略估计透射率,之后引入容差参数并设置阈值,重新计算明亮区域的透射率,从而实现对明亮区域透射率的自校正。针对于复原图像色彩较暗的问题,采用改进的线性亮度调整方法来调节图像的亮度。实验结果显示,相较于原算法而言,改进算法可以有效的对大气光值进行估计,降低明亮区域的色彩失真,复原的图像可以保持足够的亮度,同时不丢失图像的细节,视觉效果显著提高。     

帧测量率自适应分配的分布式视频压缩感知

传统分布式视频压缩感知通常对所有的非关键帧采用相同的测量率进行测量,这种测量方式并未考虑到不同帧之间的相关性具有差异性,造成帧组的重构质量不高。针对以上问题,本文首先建立一种对非关键帧进行测量率分配的模型。然后根据所建立的分配模型,利用帧间相关性提出一种快速的自适应测量率分配算法。在该算法中,以当前帧对帧组重构的贡献率为参考标准来决定该帧的测量率。实验结果表明,在相同压缩比的条件下,将本文所提出的自适应测量率分配算法应用到分布式视频压缩感知中能有效提升视频重构质量。