A Nonaliasing, Real-Time Spatial Transform Technique

A two-pass spatial transform technique that does not exhibit the aliasing artifacts associated with techniques for spatial transform of discrete sampled images is possible through the use of a complete and continuous resampling interpolation algorithm. The algorithm is complete in the sense that all the pixels of the input image under the map of the output image fully contribute to the output image. It is continuous in the sense that no gaps or overlaps exist in the sampling of the input pixels and that the sampling can be performed with arbitrary precision. The technique is real time in the sense that it can be guaranteed to operate for any arbitrary transform within a given time limit. Because of the complete and continuous nature of the resampling algorithm, the resulting image is free of the classic sampling artifacts such as graininess, degradation, and edge aliasing.     

基于多梯度的区域自适应去马赛克算法*

针对Bayer格式的彩色滤波阵列,鉴于传统去马赛克算法存在图像纹理和边缘模糊的现象,提出了一种基于多梯度的区域自适应去马赛克算法.该算法利用区域水平和垂直方向的梯度组算子来判断区域平滑程度和插值方向,从而有效地恢复绿色分量,同时根据色度差恒定原理来还原另外两个丢失的颜色信息.实验结果显示,相对于传统插值算法提高了图像的峰值信噪比,锐化了图像的纹理和边缘,改善了图像的视觉质量.     

基于Contourlet变换多聚焦图像融合方法

该方法首先提取源图像的亮度分量,采用非下采样Contourlet变换对其进行分解,通过“合成图像像素值取大”准则对高频系数进行处理得到融合决策图,并对其进行一致性校验,最后根据校验后的决策图在RGB空间进行像素点选取,得到融合图像。实验结果表明,本文方法解决了RGB空间融合方法容易导致的颜色失真,同时本方法仅对亮度分量融合,降低运算复杂度。融合图像在保留图像有用信息的同时,弥补了传统空间域方法在细节表现力上的不足,更加符合人类的视觉特征。本文融合方法还用于灰度多聚焦图像融合中,实践证明,融合效果很好。     

基于二阶Newton插值的莫尔条纹消除算法

数码相机拍摄容易受到颜色滤波阵列(CFA)采样频率的规则性图案干扰,导致输出图像带有莫尔条纹。为解决该问题,提出一种基于二阶Newton插值近似的数码图像莫尔条纹消除算法。利用小波变换提取G分量在水平和垂直方向的高频信息,通过对高频信息做频域变换模拟CFA混叠过程,进而对图像中莫尔区域及其潜在区域进行检测。针对莫尔条纹区域,利用二阶Newton插值获取G分量各方向的估计值,并对所得估计值加权平均取得丢失的G分量,再采用色差空间模型插值恢复R和B分量,最终得到消除莫尔条纹且包含完整RGB信息的图像。实验结果表明,该算法在不影响图像色彩质量的同时能有效去除莫尔条纹,且相比双线性插值、Hibbard等算法恢复图像的峰值信噪比更高、主观视觉效果更好。     

基于生成对抗网络的CFA图像去马赛克算法

在单传感器数码相机图像采集系统的彩色滤波阵列中,每个像素仅捕获单一颜色分量,并且在彩色图像重构过程中图像边缘等高频区域的伪影现象尤为明显。提出一种基于生成对抗网络的图像去马赛克算法,通过设计生成对抗网络的生成器、鉴别器和网络损失函数增强学习图像高频信息的能力,其中使用的生成器为具有残差稠密块和远程跳跃连接的深层残差稠密网络,鉴别器由一系列堆叠的卷积单元构成,并且结合对抗性损失、像素损失以及特征感知损失改进网络损失函数,提升网络整体性能。数值实验结果表明,与传统去马赛克算法相比,该算法能更有效减少图像边缘的伪影现象并恢复图像高频信息,生成逼真的重建图像。     

RETRACTED ARTICLE: Dense stereovision using mono-CCD color cameras

Most stereo algorithms are based only on an analysis of the luminance information. However, with advances in camera technology, in addition to the fact that color information can robustly improve matching, color stereovision is receiving more and more attention. Color stereovision setups are usually based on single-sensor cameras which provide color filter array (CFA) images. In these images, a single color component is sampled at each pixel rather than the three required components red, green, and blue (RGB). We show that standard demosaicing techniques, which are used to interpolate the missing components, are not well adapted when the resulting color pixels are matched in order to estimate image disparities. In order to avoid this problem while exploiting color information, we propose a new matching system designed for dense stereovision based on pairs of CFA images.     

Real-time adaptive radiometric compensation

Recent radiometric compensation techniques make it possible to project images onto colored and textured surfaces. This is realized with projector-camera systems by scanning the projection surface on a per-pixel basis. Using the captured information, a compensation image is calculated that neutralizes geometric distortions and color blending caused by the underlying surface. As a result, the brightness and the contrast of the input image is reduced compared to a conventional projection onto a white canvas. If the input image is not manipulated in its intensities, the compensation image can contain values that are outside the dynamic range of the projector. These will lead to clipping errors and to visible artifacts on the surface. In this article, we present an innovative algorithm that dynamically adjusts the content of the input images before radiometric compensation is carried out. This reduces the perceived visual artifacts while simultaneously preserving a maximum of luminance and contrast. The algorithm is implemented entirely on the GPU and is the first of its kind to run in real-time.     

Detection algorithm for color dynamic images by multiple surveillance cameras under low luminance conditions based on fuzzy corresponding map

An objects detection algorithm for color dynamic images from two cameras is proposed for a surveillance system under low illumination. It provides automatic calculation of a fuzzy corresponding map and color similarity for lower luminance conditions, which detects little chromatic regions in CCD camera images under lower illumination and presents regions with a possibility of occlusion situation. Experimental detection results for two dynamic images from real surveillance cameras in a downtown area in Japan under low luminance conditions show that the proposed algorithm has 15% improved accuracy compared with the independent detection algorithm in the same false alarm rate, which occlusion regions are correctly presented. Moreover, implementability for severe surveillance situation is discussed. The proposed algorithm is being considered for use in a low cost surveillance system at a relatively poor security downtown (shopping mall) area in Japan.     

边导向的双三次彩色图像插值

提出了一个新的边导向的双三次卷积(Cubic convolution, CC)彩色图像插值算法. 对于待插值的像素, 首先在其邻域检测两个正交方向边的强度. 如果该 像素在一个强边上, 则沿着强边的方向执行CC插值估计该像素;否则, 该像素 在弱边或纹理区域, 通过加权平均两个正交方向的CC插值估计该像素. 本文方法也考虑了彩色平面之间的相关性. 实验结果显示, 本文方法显著优于经 典的CC插值和其他一些插值方法.     

Effective demosaicking algorithm based on edge property for color filter arrays

Under the influence of digitization, digital still cameras (DSC) are becoming prevalent in recent years. The image capturing and file storing process of a digital still camera involves multiple image processing and precise corrective calculations. In order to reduce cost and volume, a digital still camera usually utilizes only one sensor and color filter array (CFA) for capturing images, and reconstructs a corresponding full-color image using a color interpolation method. Demosaicking is the first step of image processing of digital still cameras and has been integrated into the design of a variety of digital still cameras. If noise and blurred edges exist from the onset of image reconstruction, a post-processing can do little to improve the quality of the reconstructed image. A demosaicking method is proposed in this paper to prevent the occurrence of color artifacts. By detecting the edge characteristics of a digital image, accurate weights can be obtained for image interpolation, before refinement is made in post-processing. After comparing the experiment results of this paper with those of previously proposed methods, it is found that the proposed method can effectively reduce color artifacts and enhance image quality.     

一种适于移动设备的全景图快速拼接方法

针对传统移动端全景图生成方法存在拼接速度慢、消耗内存以及存在接缝和“鬼影”等问题,提出了一种适用于移动设备的全景图生成算法。算法首先对源序列图像进行色彩校正,从而保证色彩及亮度均匀,以缩短后期图像融合时间;然后在全景图拼接过程中检测“鬼影”,同时运用梯度域目标移除和区域填充方法移除“鬼影”,利用泊松融合进行拼接后的平滑处理;同时在图像拼接过程中优化了内存分配机制,以减少在图像拼接过程中的内存消耗。通过编程实现了该算法,在配置为332MHz处理器和128MB内存的手机上对不同光照下拍摄的分辨率为1280×720的照片进行测试,并与传统的全局全景图拼接算法进行对比,若利用全局全景图拼接算法对2至9张源序列图像进行拼接,内存消耗为12.3~23.6MB,而提出的算法占用更少内存,分别为9.9~14.5MB。实验结果表明该方法消除图像接缝及“鬼影”较彻底,拼接速度快且节省内存,生成的最终全景图质量较好,可用于移动设备上的全景图拼接。     

On the Parameters of Human Visual Performance: An Investigation of the Benefits of Antialiasing

A two-part experiment was conducted to investigate the effects of aliasing artifacts and screen resolution on a simple visual recognition task. The results indicate that in many cases far less realism may be necessary in synthetic computer-generated imagery than is often assumed. The first part of the experiment was a subjective rating of image quality. The second part measured the relationship between image quality and task effectiveness: Subjects were asked to discriminate between images of two types of objects similar to objects used in experiments involving mental rotation. At higher resolutions, the elimination of aliasing artifacts did not significantly improve subjects' performances. At intermediate and low resolutions, comparable to what might be used for iconic menus, the reduction in aliasing artifacts improved performance. The subjective ratings indicate that for both high and low resolution the elimination of aliasing artifacts does not improve quality, whereas images rendered at intermediate resolutions are significantly degraded by aliasing artifacts to the extent that antialiasing improves the subjective rating. These results are interpreted in the context of an ongoing research program aimed at identifying the parameters of real-time human performance for graphics workstations.     

Subband Synthesis for Color Filter Array Demosaicking

This paper presents a new algorithm for demosaicking images captured through a color filter array (CFA). The objective of the CFA demosaicking is to render a full color image from the mosaicked image. This is commonly achieved by estimating the missing color information from the surrounding observed pixels. In this paper, we integrate the observation that color images have a strong intrachannel spatial correlation in low-frequency components and a dominant interchannel correlation in the high- frequency components. A new framework is proposed to utilize this information where the missing pixels in each color channel are estimated from the wavelet subbands. A modified median-filtering operation is then applied in the subband domain. The algorithm is adaptive and produces superior full-resolution images when compared with other methods.     

Local MAP estimation for quality improvement of compressed color images

This paper concerns color image restoration aiming at objective quality improvement of compressed color images in general rather than merely artifact reduction. In compressed color images, colors are usually represented by luminance and chrominance components. Considering characteristics of human vision system, chrominance components are generally represented more coarsely than luminance component. To recover such chrominance components, we previously proposed a model-based chrominance restoration algorithm where color images are modeled by a Markov random field. This paper presents a color image restoration algorithm derived by the MAP estimation, where all components are totally estimated. Experimental results show that the proposed restoration algorithm is more effective than the previous one.     

单幅雾天图像的同步去噪与复原

在单幅雾天图像复原中图像不可避免地存在大量噪声,这会对复原结果带来很大影响。文中提出一种基于联合双边滤波的单幅雾天图像同步去噪和复原算法。该算法首先根据暗通道先验假设估计出可反映场景深度特性的初始传输图。其次,利用联合双边滤波器,在原始图像的引导下对初始的粗糙传输图进行细化,有效降低光晕现象的出现。再使用一次双边滤波求解复原图像,在得到去雾图像的同时实现图像去噪。最后,在滤波过程中引入一个色彩恢复因子,解决复原过程引起的色彩失真问题。文中对各种类型的图片进行对比实验,结果表明该算法能在去雾的同时有效抑制图像中的噪声,并保持较低的计算复杂度。此外,引入的色彩恢复因子也给复原图像带来丰富的色彩。     

基于色阶重建的彩色图像增强算法

色阶重建技术已应用于高动态范围图像的处理中.应用色调映射技术和局部亮度适应性原理,提出了彩色图像增强的算法来改善彩色图像的可视性.使用一个参数自适应的可变基的对数函数对亮度图像进行变换,采用双边滤波技术来计算每一像素的局部适应亮度,再对彩色图像的对比度进行调整.实验结果显示,图像的暗区部分获得了较好的可视性和细节,同时对比度获得改善.     

图像内容感知缩放的检测方法研究

针对可用于图像篡改的内容感知缩放技术,本文提出了一种基于概率Map图统计特征的内容感知缩放检测算法.该算法利用概率Map图来反映图像是否经过内容感知缩放操作,并利用新提出的积分投影与局部统计特征来检测篡改图像.而后利用分类器进行分类训练,从而有效识别基于内容感知缩放操作的图像篡改.实验结果显示,所提算法能够区分出原始图像与篡改图像,并具有较高的正确检测率.     

基于亮度分区模糊融合的高动态范围成像算法

针对单幅图像生成高动态范围（HDR）图像进行直方图扩展时,造成的色彩失真、局部细节信息丢失的问题,提出了一种基于亮度分区融合的高动态范围图像成像算法。首先,提取正常曝光彩色图像的亮度分量,根据亮度阈值将亮度分成两个区间;然后,对两个区间的图像用改进的指数函数扩展其亮度范围,使得低亮度区域的亮度增加、范围扩大,高亮度区域的亮度减小、范围扩大,从而增大图像的整体对比度,保留色彩和细节信息;最后,将扩展后的图像和原始正常曝光的图像基于模糊逻辑的方法融合为高动态图像。分别从主观和客观两方面对所提算法进行了分析。实验结果表明,所提算法能够有效地扩展图像的亮度范围,并保持场景的颜色信息和细节信息,生成的图像视觉效果更佳。     

新的基于边缘检测提高图像质量的插值算法

通过对经典插值算法放大后得到的高分辨率图像先进行边缘检测,然后根据多方向边缘梯度变化,对边缘邻接像素点进行自相关模糊消除修正插值;多倍放大时采取小倍率循环修正方式实现.实验证明,算法克服了经典插值后图像的边缘锯齿和模糊效应,使修正优化后的图像清晰.对于RGB彩色图像先进行各分量色融合,再边缘检测,对提高图像质量也取得了明显的效果.