基于CCD图像传感器的彩色插值算法研究

本文讨论了基于Bayer颜色滤波阵列的CCD图像传感器的几种彩色插值算法,包括邻域插值、线性插值、相关线性插值、立方卷积插值等。本文研究了基于绿色分量的CCD彩色插值算法,并对其进行了改进。基于本文方法进行实验,结果显示,可以改善普通线性插值算法引起的图像模糊。     

三维B超医学图像插值重构研究

本文讨论了图像插值在超声图像三维重建中的应用,着重于在进行可视化之前如何利用合适的插值算法把三维规则不均匀数据场转换为三维规则均匀数据场,提出有偏距离加权同心插值,并从峰值信噪比、归一化互相关系数和运算速度等几个方面与线性插值,三次插值以及高斯插值等三种较典型的插值算法进行了比较。     

一种基于绿色分量的CCD颜色插值算法

提出了基于Bayer阵列的CCD颜色插值算法。在CCD阵列中,接收G色分量的像素占据总像素的一半,所以其中包含的影像信息对红（R)色、蓝（B）色分量的插值具有参考作用。本文以此为依据,首先利用中值插值算法对G色分量进行插值处理,然后参照G色分量的分布,插值产生R色和B色分量。基于此方法进行实验,结果显示,可以改善普通线性插值算法引起的图像模糊。     

基于FPGA的高分辨实时监控图像缩放设计

介绍了一种基于图像的双三次线性插值缩放算法的设计方法,并通过FPGA验证了设计的可行性.重点讨论了视频缩放的插值算法,对两种实现方法在硬件资源利用率及实施效率方面进行了比较并论证了块状插值实现方法的优越性.最终设计实现了高分辨率实时视频图像的缩放.     

几种图像缩放算法的研究

时域内常用的几种图像缩放算法有：最近邻插值、线性插值、二次插值、三次插值、拉格朗日插值、高斯插值等,对这些算法的性能进行分析比较,综合通带、阻带及截止频率,最近邻插值和线性插值应该避免,高斯基函数（N较大者）具有较好性能;并且在频域内研究了二维可分离插值滤波器和不可分离插值滤波器,这两种方法以对通带和阻带的要求作为优化目标,以滤波器的结构为约束条件,将滤波器的设计转化为一个约束优化问题进行解决;实验结果表明二维不可分离插值滤波器的方法图像缩放后的效果最好。     

区域指导的自适应图像插值算法

提出了一种以图像区域的一致性为指导的自适应选择插值公式(简称ADA)的图像插值算法.首先将图像分割成不同区域,然后结合近邻法和众数法确定待插值点所属区域,将待插值点分类处理:对于区域内的点,采用线性插值;对于区域间的过渡点,设计非线性插值公式,分配较大的权值给与待插值点属于同一区域的邻域像素,分配较小的权值给其它邻域像素.实验表明,ADA图像插值算法得到的插值图像有较好的主观图像质量,其峰值信噪比(PSNR)比传统方法平均提高了3.11～4.39 dB.     

基于抽样随机一致性和图像特征的图像配准

文中介绍了一种基于Harris角点检测图像特征的图像配准方法。通过随机一致性算法(RANSAC)和正则化交叉相关测度建立了待配准图像和模板图像之间特征点的对应关系。分别采用仿射变换和透射变换通过该对应关系进行图像配准。在完成配准后,分别采用自适应B样条插值算法和多项式插值算法对配准后的图像进行校正。采用信噪比和交叉相关系数作为评价标准,通过对几种方法相结合的图像处理结果进行比较,得出结论:对应超声TOFD图像,仿射变换结合B样条插值校正的方法可以得到最好的效果。     

基于颜色衰减先验和视觉显著性的水下图像增强算法

针对水下拍摄的图片存在颜色失真、细节和边缘模 糊等特点,提出了一种基于颜色衰减先验的水下图像增强算法。首先在计算暗通道函数时,用最小值滤波去噪。然后,对图片进行显著图处理,利用颜色先验法则完成深度估计。此滤波方法不仅能降噪,还可以防止颜色失真。最后,基于模型简化获得复原的图片,将其进行伽马变换进行校正,实现柔性去雾。实验结果表明,本文算法与几种典型的水下图像去雾算法相比,能够较好提高图像的清晰度和对比度,同时获得较好的图像颜色。     

CFA图像插值算法在DSP实时系统上的应用

彩色滤波阵列(CFA)格式的图像需要进行图像插值,才能获得全彩色图像.但是,由于相机噪声的存在,不仅该点的彩色信息失真,同时还会影响邻近像素利用该点信息进行插值的图像颜色.为了得到高质量的全彩色图像,采用了一种基于梯度的滤噪插值算法,在保留了图像边缘和细节信息的同时,滤除了噪声,使图像的信噪比提高了近1倍;由于这种数字相机没有白平衡处理,使插值恢复的彩色图像颜色与实际景物相比存在较大差异,为了消除这种差异,实现了自适应白平衡校正方法,使图像方差提高了近20％,与人眼观察到实际景物的颜色更接近;整个彩色编码算法应用了DSP的并行流水线技术,保证了其在DSP上运行的实时性,处理频率达到25Hz.     

基于顺序形态学的医学图像插值算法的研究

医学图像插值方法是医学图像三维重建的关键技术,插值的结果直接决定了三维重建的效果.本文在深入研究了顺序形态学理论的基础上,提出了一种基于顺序形态学的插值算法,该算法采用百分位膨胀和腐蚀算子,解决了线性插值和形状插值等算法产生的边界模糊的问题.仿真实验表明该算法产生的断层图像和原有断层图像过渡自然,为后续的医学图像三维重建奠定了基础.     

Saturation-based adaptive inverse gradient interpolation for Bayerpattern images

A saturation-based adaptive gradient interpolation algorithm is developed for Bayer pattern images obtained in single-sensor digital cameras. The proposed algorithm uses the concept of pseudo-saturation for 2×2 blocks of the image. The algorithm adapts the scheme of interpolation based on the classification of the pseudo-saturation of Bayer pattern images. Compared with other algorithms, the proposed method preserves fine details, reduces false colours, and distortions around edges and sharp features of the colour image reconstructed from different kinds of Bayer pattern images     

A MODIFIED EDGE-ORIENTED SPATIAL INTERPOLATION ALGORITHM FOR CONSECUTIVE BLOCKS ERROR CONCEALMENT

This paper proposes a low-complexity spatial-domain Error Concealment （EC） algorithm for recovering consecutive blocks error in still images or Intra-coded （I） frames of video sequences. The proposed algorithm works with the following steps. Firstly the Sobel operator is performed on the top and bottom adjacent pixels to detect the most likely edge direction of current block area. After that one-Dimensional （1D） matching is used on the available block boundaries. Displacement between edge direction candidate and most likely edge direction is taken into consideration as an important factor to improve stability of 1D boundary matching. Then the corrupted pixels are recovered by linear weighting interpolation along the estimated edge direction. Finally the interpolated values are merged to get last recovered picture. Simulation results demonstrate that the proposed algorithms obtain good subjective quality and higher Peak Signal-to-Noise Ratio （PSNR） than the methods in literatures for most images.     

Camera Exposure Determination Based on a Psychometric Quality Model

This paper discusses exposure determination for digital cameras, beginning with a tutorial on exposure control and the factors affecting the quality of still captures, especially exposure index. It presents a psychometric model for the overall quality of digital camera images as a function of motion blur and exposure index. A motion-aware exposure control algorithm based on that model is described for exclusively ambient illumination and then extended for combined flash and ambient illumination. Results from a field test with a modified consumer camera quantify the effectiveness of the algorithm.     

Noise reduction using multiscale bilateral decomposition for digital color images

In this paper, we propose a noise reduction algorithm for digital color images using a nonlinear image decomposition approach. Most existing noise reduction methods do not adequately consider spatial correlation of color noise in digital color images. Color noise components in color images captured by digital cameras are observed as irregular grains with various sizes and shapes, which are spatially randomly distributed. We use a modified multiscale bilateral decomposition to effectively separate signal and mixed-type noise components, in which a noisy input image is decomposed into a base layer and several detail layers. A base layer contains strong edges, and most of noise components are contained in detail layers. Noise components in detail layers are reduced by an adaptive thresholding function. We obtain a denoised image by combining a base layer and noise-reduced detail layers. Experimental results show the effectiveness of the proposed algorithm, in terms of both the peak signal-to-noise ratio and visual quality.     

New edge-directed interpolation

This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.     

Adaptive visually lossless JPEG-based color image compression

The paper presents two approaches to adaptive JPEG-based compression of color images inside digital cameras. Compression for both approaches, although lossy, is organized in such a manner that introduced distortions are not visible. This is done taking into account quality of each original image before it is subject to lossy compression. Noise characteristics and blur are assumed to be the main factors determining visual quality of original images. They are estimated in a fast and blind (automatic) manner for images in RAW format (first approach) and in Bitmap (second approach). The dominant distorting factor which can be either noise or blur is determined. Then, the scaling factor (SF) of JPEG quantization table is adaptively adjusted to preserve valuable information in a compressed image with taking into account estimated noise and blur influence. The advantages and drawbacks of the proposed approaches are discussed. Both approaches are intensively tested for real-life images. It is demonstrated that the second approach provides more accurate estimate of degrading factor characteristics, and thus, a larger compression ratio (CR) increase compared to super-high quality (SHQ) mode used in consumer digital cameras. The first approach mainly relies on the prediction of noise and blur characteristics to be observed in Bitmap images after a set of nonlinear operations applied to RAW data in image processing chain. It is simpler and requires less memory but appeared to be slightly less beneficial. Both approaches are shown to provide, on the average, more than two times increase in average CR compared to SHQ mode without introducing visible distortions with respect to SHQ compressed images. This is proven by the analysis of modern visual quality metrics able to adequately characterize compressed image quality.     

Robust Color Demosaicking With Adaptation to Varying Spectral Correlations

Almost all existing color demosaicking algorithms for digital cameras are designed on the assumption of high correlation between red, green, blue (or some other primary color) bands. They exploit spectral correlations between the primary color bands to interpolate the missing color samples, but in areas of no or weak spectral correlations, these algorithms are prone to large interpolation errors. Such demosaicking errors are visually objectionable because they tend to correlate with object boundaries and edges. This paper proposes a remedy to the above problem that has long been overlooked in the literature. The main contribution of this work is a hybrid demosaicking approach that supplements an existing color demosaicking algorithm by combining its results with those of adaptive intraband interpolation. This is formulated as an optimal data fusion problem, and two solutions are proposed: one is based on linear minimum mean-square estimation and the other based on support vector regression. Experimental results demonstrate that the new hybrid approach is more robust and eliminates the worst type of color artifacts of existing color demosaicking methods.      

Perceptual assessment of demosaicing algorithm performance

Demosaicing is an important part of the image-processing chain for many digital color cameras. The demosaicing operation converts a raw image acquired with a single sensor array, overlaid with a color filter array, into a full-color image. In this paper, we report the results of two perceptual experiments that compare the perceptual quality of the output of different demosaicing algorithms. In the first experiment, we found that a Bayesian demosaicing algorithm produced the most preferred images. Detailed examination of the data, however indicated that the good performance of this algorithm was at least in part due to the fact that it sharpened the images while it demosaiced them. In a second experiment, we silenced image sharpness as a factor by applying a sharpening algorithm to the output of each demosaicing algorithm. The optimal amount of sharpening to be applied to each image was chosen using the results of a preliminary experiment. Once sharpness was equated in this way, an algorithm developed by Freeman based on bilinear interpolation combined with median filtering, gave the best results. An analysis of our data suggests that our perceptual results cannot be easily predicted using an image metric     

JPEG压缩对噪声相关性影响的修正

提出了一种修正JPEG压缩对噪声相关性大小影响的新方法。该方法应用最小二乘法估计原理求出噪声相关性大小与品质因素的函数关系,根据修正的约束条件和归一化条件推导出修正函数,利用该修正函数把各种品质因素下的噪声相关性大小修正为最高品质因素下的噪声相关性大小,使得判别阈值与品质因素无关,从而修正了JPEG压缩对噪声相关性大小的影响。实验结果表明,利用该方法产生的错误拒绝率很小,但错误接受率随着品质因素的变小而变大。