基于光滑0范数压缩感知的多光谱图像去马赛克算法

提出了一种基于压缩感知(CS)的多光谱滤波阵列(MSFA)的多光谱图像去马赛克算法(DMA)。 首先,通过将MSFA采样得 到马赛克图像的过程等效为CS理论中的感知矩阵采样的过程,并充分利用多光谱图 像的空间和谱间 相关性,通过在三维空间傅里叶基上对多光谱图像进行稀疏表示;然后由随机MSFA模式和CS 理论构造的测量矩阵对多光谱图像进行观测投影,最后采用CS重构算法求解0范 数下的最优化问 题,从而得到多光谱图像的稀疏表示系数。给出对算法性能的评估数据和Matlab仿真 图片。实验结果证明,本文算法的峰值信噪比(PSNR)值高于克罗内克CS(KCS)和组稀疏(GS)两种算法,且有效地减少了上述两种算法中出现的模糊现 象,改善了图像的视觉效果。     

双分支残差去马赛克网络

为了缓解传统拜耳型去马赛算法中常出现的拉链和伪影等问题,提出一个新颖的基于深度学习的去马赛克算法。所提算法首先对马赛克图像中的红色、绿色及蓝色通道中的像素进行分解、剔除及组合等操作得到两幅彩色图像,然后将这两幅彩色图像输入到设计的卷积神经网络中,以重建出完整的彩色图像,该网络能充分地利用卷积层所生成的特征信息。实验结果表明,所提算法重建出的完整彩色图像的质量相对较高,并且在一定程度上缓解了拉链和伪影等问题,其客观指标和主观评价都优于对比算法。     

基于压缩感知的多光谱图像去马赛克算法

针对目前多光谱图像去马赛克算法存在计算量大、效率低的缺点,本文提出一种基于压缩感知的多光谱图像去马赛克算法。首先,分析去马赛克与压缩感知问题的等价性,建立基于压缩感知的去马赛克模型;然后,采用离散余弦变换构建压缩感知的稀疏基,将去马赛克问题转化为压缩感知的信号重构问题;最后,采用改进的光滑0范数和修正牛顿法的重构算法求解去马赛克问题,得到重构的多光谱图像。仿真实验表明,相对于基于克罗内克压缩感知和组稀疏两种算法,本文算法提高了重构的多光谱图像的峰值信噪比,能有效减少对比算法重构多光谱图像中出现的锯齿现象,改善了重构图像具有更好的视觉效果。实验结果验证了本文算法的有效性。     

改进的联合去马赛克与放大算法及其硬件实现

为了从单CCD/CMOS图像探测器相机拍摄的Bayer格式图像恢复出更高分辨率全彩色图像,设计了一种改进的联合去马赛克与放大算法。首先,估计原始Bayer格式图像中丢失的绿色通道信息,根据颜色梯度的比较结果,将水平或垂直方向的绿色通道信息作为彩色图像的G分量,并根据彩色图像颜色通道之间高度相关的特性,计算颜色差R-G和B-G;然后,将去马赛克过程中的梯度方法继续用于确定丢失的绿色像素,放大绿色通道;最后,对颜色差值图像R-G和B-G进行放大,即可恢复出红色和蓝色通道信息,得到放大的全彩色图像。试验结果表明,算法恢复出的高分辨率彩色图像的峰值信噪比(PSNR)有较明显提升,且主观目视清晰、边缘锐利,特别是硬件实现方面能够做到实时处理。目前,本文算法已经在Xilinx公司的FPGA上实现,并用于工程实践。满足了项目实际需要。     

基于主成分分析和引导滤波的色调映射算法

针对传统高动态范围(HDR)图像色调映射算法普遍 存在色偏和效率低的问题,提出了一种基于主成分 分析(PCA)进行色彩空间转换和具有边缘保持特性的引导滤波图像增强的色调映射算法。首 先利用图像的平均亮 度实现图像Gamma自适应校正;然后采用PCA方法将图像的3个颜色分量转换为相 互正交的亮度 分量和色差分量,模拟相机响应函数(CRF)曲线压缩亮度分量的动态范围,并将结果与加权 后的色差分量联合, 经PCA逆转换得到新的RGB分量;最后利用引导滤波方法对图像进行增强,提高 图像对比度,避 免光晕现象。实验结果表明,本文算法效率比经典的iCAM06算法提高了4倍以 上,并能有效避免偏色和 光晕现象,处理后图像的细节清晰层次感强、色彩自然丰富,不仅能用于传统的便携式显示 设备,而且可用于穿戴式智能视觉辅助设备和无屏幕显示器等新兴领域。     

改进的联合去马赛克与放大算法及其硬件实现

为了从单CCD/CMOS图像探测器相机拍摄的Bayer 格式图像恢复出更高分辨率全彩色图像, 设计了一种改进的联合去马赛克与放大算法。首先,估计原始Bayer格式图像中丢失的绿色 通道信息, 根据颜色梯度的比较结果,将水平或垂直方向的绿色通道信息作为彩色图像的 G分量,并根据彩色图 像颜色通道之间高度相关的特性,计算颜色差R-G 和B-G;然后,将去马赛克过程中的梯度方法继续用于确定丢失的绿色像素,放大绿色通道 ;最后, 对颜色差值图像R-G和B-G进行放大,即可恢复出红色和蓝色通道 信息,得到放大的全彩色图 像。试验结果表明,算法恢复出的高分辨率彩色图像的峰值信噪比(PSNR)有较明显提 升,且主观目视清晰、边缘锐 利,特别是硬件实现方面能够做到实时处理。目前,本文算法已经在Xilinx公 司的FPGA上实现,并用于工程实践。满足了项目实际需要。     

非局部相似性去马赛克算法

单传感器数码相机得到的色彩图像在每一个像素点处只有一种色彩值,为了得到一幅全彩色图像,需要在每一个像素位置上估计出另外两个缺失的色彩值。现有主要算法都是利用像素的相关性进行估计和插值,在那些边缘色彩跳变处和色彩高饱和度处容易估计失误,出现所谓的马赛克失真。为了克服这类马赛克现象,本文提出了一种利用图像的非局部相似性,即利用处于图像中不同位置处的像素点往往表现出很强的相关性这一特点,结合图像内容的局部平坦度自适应去马赛克的插值算法。该算法,首先根据相似度函数搜索与被插像素最相似的像素,然后利用区域水平和垂直方向的梯度组算子来计算区域的平坦度,从而根据相似程度和平坦度自适应地选择图像块进行插值。实验结果表明,相对于传统插值算法,该算法提高了图像的峰值信噪比,锐化了图像的纹理和边缘,减少了虚假色和锯齿现象,改善了图像的视觉效果。      

基于 FPGA 的彩色图像实时采集系统设计

为了从单片Bayer格式图像探测器获取高质量实时彩色图像,研究并设计了一套基于FPGA硬件的彩色图像实时采集系统。介绍了针对MT9M011图像探测器的寄存器配置时序的设计。重点研究了影响彩色图像质量的Bayer格式图像去马赛克算法,该算法首先对Bayer格式原图进行二阶拉普拉斯修正,利用色差和梯度关系确定丢失的绿色分量,再结合给定像素周围的4个色差进行插值计算,分别得到丢失的红色分量和蓝色分量,从而完成彩色图像的恢复。设计了基于千兆以太网(GigE)的图像传输机制,使系统数据吞吐率大大提高。实验数据表明,系统插值算法在峰值信噪比(PSNR)方面相比双线性插值,R、G、B三通道均提高约15dB左右,图像目视细节丰富,伪彩色得到有效抑制;在分辨率为1 280×1 024、帧频为15f/s的情况下,可以实时输出全彩色图像。目前,系统已经工程应用,其优异表现满足了项目实际需要。     

基于DWT-QDFT的硬拷贝彩色图像全息水印

提出了一种基于离散小波变换(DWT)及四元数傅里 叶变换(QDFT)的鲁棒性全息水印算法。首先对RGB彩色图像的三个色彩分量分 别做DWT,取变换后的3个低频部分组成纯四元数矩阵;随后对其进行QDFT ,将全息水印通过替代QDFT的实部的中频系数进行嵌入,再经过逆QDFT及小波重构得到含有 水印的彩 色图像。攻击测试结果表明,本文算法对于常规的信号处理有很好的鲁棒性;打印扫描实验 证明,本文算法具有抵 抗硬拷贝攻击的能力。四元数傅立叶变换的引入,实现了小波低频系数的嵌入保证了算法的 鲁棒性,同时 又结合了四元数在处理彩色图像中的优势,双变换域的采用与全息方法的结合实现了算法应 用于硬拷贝彩 色图像中鲁棒性及不可见性之间的平衡。本文全息水印方案可以应用于印品水印防伪领域。     

基于多尺度对比度塔和方向滤波器组的图像融合

本文从图像的特性出发,将方向信息与多分辨分析相结合,提出了基于方向滤波器组的融合思想.进而考虑到人眼的视觉特性,利用对比度塔分解(CP)捕获图像的低频分量,利用方向滤波器组(DFB)获得图像的高频分量,提出了一种基于多尺度对比度塔和方向滤波器组(CPDFB)的图像融合方法,同时分析了该方法的融合机理和计算复杂度,较好地实现了复杂图像的融合.仿真实验验证了其可行性和有效性.     

Spatio-spectral color filter array design for optimal image recovery

In digital imaging applications, data are typically obtained via a spatial subsampling procedure implemented as a color filter array-a physical construction whereby only a single color value is measured at each pixel location. Owing to the growing ubiquity of color imaging and display devices, much recent work has focused on the implications of such arrays for subsequent digital processing, including in particular the canonical demosaicking task of reconstructing a full color image from spatially subsampled and incomplete color data acquired under a particular choice of array pattern. In contrast to the majority of the demosaicking literature, we consider here the problem of color filter array design and its implications for spatial reconstruction quality. We pose this problem formally as one of simultaneously maximizing the spectral radii of luminance and chrominance channels subject to perfect reconstruction, and-after proving sub-optimality of a wide class of existing array patterns-provide a constructive method for its solution that yields robust, new panchromatic designs implementable as subtractive colors. Empirical evaluations on multiple color image test sets support our theoretical results, and indicate the potential of these patterns to increase spatial resolution for fixed sensor size, and to contribute to improved reconstruction fidelity as well as significantly reduced hardware complexity.     

Color filter array demosaicking: new method and performance measures

Single-sensor digital cameras capture imagery by covering the sensor surface with a color filter array (CFA) such that each sensor pixel only samples one of three primary color values. To render a full-color image, an interpolation process, commonly referred to as CFA demosaicking, is required to estimate the other two missing color values at each pixel. In this paper, we present two contributions to the CFA demosaicking: a new and improved CFA demosaicking method for producing high quality color images and new image measures for quantifying the performance of demosaicking methods. The proposed demosaicking method consists of two successive steps: an interpolation step that estimates missing color values by exploiting spatial and spectral correlations among neighboring pixels, and a post-processing step that suppresses noticeable demosaicking artifacts by adaptive median filtering. Moreover, in recognition of the limitations of current image measures, we propose two types of image measures to quantify the performance of different demosaicking methods; the first type evaluates the fidelity of demosaicked images by computing the peak signal-to-noise ratio and CIELAB /spl utri/E/sup *//sub ab/ for edge and smooth regions separately, and the second type accounts for one major demosaicking artifact-zipper effect. We gauge the proposed demosaicking method and image measures using several existing methods as benchmarks, and demonstrate their efficacy using a variety of test images.     

Binary tree-based generic demosaicking algorithm for multispectral filter arrays.

In this paper, we extend the idea of using mosaicked color filter array (CFA) in color imaging, which has been widely adopted in the digital color camera industry, to the use of multispectral filter array (MSFA) in multispectral imaging. The filter array technique can help reduce the cost, achieve exact registration, and improve the robustness of the imaging system. However, the extension from CFA to MSFA is not straightforward. First, most CFAs only deal with a few bands (3 or 4) within the narrow visual spectral region, while the design of MSFA needs to handle the arrangement of multiple bands (more than 3) across a much wider spectral range. Second, most existing CFA demosaicking algorithms assume the fixed Bayer CFA and are confined to properties only existed in the color domain. Therefore, they cannot be directly applied to multispectral demosaicking. The main challenges faced in multispectral demosaicking is how to design a generic algorithm that can handle the more diversified MSFA patterns, and how to improve performance with a coarser spatial resolution and a less degree of spectral correlation. In this paper, we present a binary tree based generic demosaicking method. Two metrics are used to evaluate the generic algorithm, including the root mean-square error (RMSE) for reconstruction performance and the classification accuracy for target discrimination performance. Experimental results show that the demosaicked images present low RMSE (less than 7) and comparable classification performance as original images. These results support that MSFA technique can be applied to multispectral imaging with unique advantages.     

Context-aware joint dictionary learning for color image demosaicking

Most digital cameras are overlaid with color filter arrays (CFA) on their electronic sensors, and thus only one particular color value would be captured at every pixel location. When producing the output image, one needs to recover the full color image from such incomplete color samples, and this process is known as demosaicking. In this paper, we propose a novel context-constrained demosaicking algorithm via sparse-representation based joint dictionary learning. Given a single mosaicked image with incomplete color samples, we perform color and texture constrained image segmentation and learn a dictionary with different context categories. A joint sparse representation is employed on different image components for predicting the missing color information in the resulting high-resolution image. During the dictionary learning and sparse coding processes, we advocate a locality constraint in our algorithm, which allows us to locate most relevant image data and thus achieve improved demosaicking performance. Experimental results show that the proposed method outperforms several existing or state-of-the-art techniques in terms of both subjective and objective evaluations.     

PCA-Based Spatially Adaptive Denoising of CFA Images for Single-Sensor Digital Cameras

Single-sensor digital color cameras use a process called color demosaicking to produce full color images from the data captured by a color filter array (CFA). The quality of demosaicked images is degraded due to the sensor noise introduced during the image acquisition process. The conventional solution to combating CFA sensor noise is demosaicking first, followed by a separate denoising processing. This strategy will generate many noise-caused color artifacts in the demosaicking process, which are hard to remove in the denoising process. Few denoising schemes that work directly on the CFA images have been presented because of the difficulties arisen from the red, green and blue interlaced mosaic pattern, yet a well designed “denoising first and demosaicking later” scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. This paper presents a principle component analysis (PCA) based spatially-adaptive denoising algorithm, which works directly on the CFA data using a supporting window to analyze the local image statistics. By exploiting the spatial and spectral correlations existed in the CFA image, the proposed method can effectively suppress noise while preserving color edges and details. Experiments using both simulated and real CFA images indicate that the proposed scheme outperforms many existing approaches, including those sophisticated demosaicking and denoising schemes, in terms of both objective measurement and visual evaluation.      

Adaptive filtering for color filter array demosaicking.

Most digital still cameras acquire imagery with a color filter array (CFA), sampling only one color value for each pixel and interpolating the other two color values afterwards. The interpolation process is commonly known as demosaicking. In general, a good demosaicking method should preserve the high-frequency information of imagery as much as possible, since such information is essential for image visual quality. We discuss in this paper two key observations for preserving high-frequency information in CFA demosaicking: (1) the high frequencies are similar across three color components, and (2) the high frequencies along the horizontal and vertical axes are essential for image quality. Our frequency analysis of CFA samples indicates that filtering a CFA image can better preserve high frequencies than filtering each color component separately. This motivates us to design an efficient filter for estimating the luminance at green pixels of the CFA image and devise an adaptive filtering approach to estimating the luminance at red and blue pixels. Experimental results on simulated CFA images, as well as raw CFA data, verify that the proposed method outperforms the existing state-of-the-art methods both visually and in terms of peak signal-to-noise ratio, at a notably lower computational cost.     

Color reproduction from noisy CFA data of single sensor digital cameras.

Single sensor digital color still/video cameras capture images using a color filter array (CFA) and require color interpolation (demosaicking) to reconstruct full color images. The color reproduction has to combat sensor noises which are channel dependent. If untreated in demosaicking, sensor noises can cause color artifacts that are hard to remove later by a separate denoising process, because the demosaicking process complicates the noise characteristics by blending noises of different color channels. This paper presents a joint demosaicking-denoising approach to overcome this difficulty. The color image is restored from noisy mosaic data in two steps. First, the difference signals of color channels are estimated by linear minimum mean square-error estimation. This process exploits both spectral and spatial correlations to simultaneously suppress sensor noise and interpolation error. With the estimated difference signals, the full resolution green channel is recovered. The second step involves in a wavelet-based denoising process to remove the CFA channel-dependent noises from the reconstructed green channel. The red and blue channels are subsequently recovered. Simulated and real CFA mosaic data are used to evaluate the performance of the proposed joint demosaicking-denoising scheme and compare it with many recently developed sophisticated demosaicking and denoising schemes.     

Novel color demosaicking for noisy color filter array data

Single sensor digital color still/video cameras use color demosaicking to reproduce full color images from color filter array (CFA) data. The quality of interpolated image will be degraded due to the sensor noise introduced during the image capture process. Many conventional demosaicking-denoising solutions adopt the channel-dependent noise model, which may fit the CMOS/CCD image sensor less than signal-dependent noise model. In this paper, the wavelet sub-band decomposition and synthesis are applied to interpolate the CFA data with signal-dependent noise model. The major contributions of this work include: (1) The combination of LMMSE and statistical calculation in wavelet domain are utilized to suppress the signal-dependent noise, which is separated into additive noise and multiplicative noise. (2) In CFA data, it has been verified that the quantitative relationship between the current pixel and the adjacent pixel, which locate in the same edge. Both simulated and real CFA images are employed to compare the proposed algorithm with the state-of-the-art techniques reported in the literature. The experimental results confirm that our method outperforms them both on demosaicking performance and on computational cost, when they process the noisy color filter array data.     

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.