Adaptive resampling algorithm for image zooming

Applying an interpolation function indiscriminately to an image, to resample it, will generally result in aliasing, edge blurring and other artefacts. The authors present an adaptive resampling algorithm for zooming up images. The algorithm is based on analysing the local structure of the image and applying a near optimal and least time-consuming resampling function will preserve edge locations and their contrast. This is done by segmenting the image dynamically into homogeneous areas, as it is scanned or received. Based on the location of the pixel to be computed (whether it is within a homogenous area, is on its edge or is an isolated feature), interpolation, extrapolation or pixel replication is chosen. The algorithm performance, from both a quality and a computational complexity aspect, are compared to different methods and functions previously reported in the literature. The advantage of the algorithm is quite apparent at edges and for large zooming factors     

Fuzzy-adapted linear interpolation algorithm for image zooming

This work presents a novel fuzzy linear interpolation algorithm with application in image zooming. Fuzzy logics are employed to derive suitable weights for the neighboring samples in the interpolation formulae. By considering local gradients to calculate the weights, the accuracy of the interpolated value is improved. Additionally, a modification of the proposed algorithm based on the interpolation error theorem is developed to deal with images containing ridges and valleys. Both quantitative results obtained by measuring the peak signal-to-noise ratio (PSNR) and perceptual observations assessed the superior performance of the proposed algorithm and its modified version with respect to the state-of-the-art interpolation methods.     

A low-complexity joint color demosaicking and zooming algorithm for digital camera.

This paper presents a low complexity joint color demosaicking and digital zooming algorithm for single-sensor digital cameras. The proposed algorithm directly extracts edge information from raw sensor data for interpolation in both demosaicking and zooming to preserve edge features in its output. This allows the extracted information to be exploited consistently in both stages and also efficiently, as no separate extraction process is required in different stages. The proposed algorithm can produce a zoomed full-color image as well as a zoomed Bayer color filter array image with outstanding performance as compared with conventional approaches which generally combine separate color demosaicking and digital zooming schemes.     

The error-amended sharp edge (EASE) scheme for image zooming.

This paper proposes a new interpolation method, called the error-amended sharp edge (EASE) scheme, which is a modified bilinear method. In order to remove/reduce interpolation artifacts such as image blur and the checkerboard effect (ringing), EASE tries to amend the interpolation error by employing the classical interpolation error theorem in an edge-adaptive fashion. EASE is applied for image zooming by both integer and noninteger magnification factors. The new interpolation scheme has proved to result in high-resolution images having clearer and sharper edges than linear interpolation methods, for all synthetic and natural images we have tested. EASE can be implemented with ease; it turns out to be similarly efficient as cubic interpolation schemes.     

A fully automatic one-scan adaptive zooming algorithm for color images

We present an interpolation algorithm for adaptive color image zooming. The algorithm produces the magnified image in one scan of the input image, and is fully automatic since does not involve any a priori fixed threshold. Given any integer zooming factor n, each pixel of the input image generates an n×n block of pixels in the zoomed image. For the currently visited pixel of the input image, the pixels of its associated block are first assigned tentative values, which are then adaptively updated before building the next block. The method is suggested for RGB images, but can equally be employed in other color spaces. Peak signal to noise ratio (PSNR) and Structural SIMilarity (SSIM) are used to evaluate the performance of the algorithm.     

Comparison and optimization of methods of color image quantization

Color image quantization, the process of reducing the number of colors in a digital color image, has been widely studied for the last fifteen years. The different steps of clustering methods are studied. The methods are compared step by step and some optimizations of the algorithms and data structures are given. A new color space called H1H2H3 is introduced, which improves the quantization heuristics. A low-cost quantization scheme is proposed.     

An optimal fuzzy system for color image enhancement.

A Gaussian membership function is proposed to fuzzify the image information in spatial domain. We introduce a global contrast intensification operator (GINT), which contains three parameters, viz., intensification parameter t, fuzzifier fh, and the crossover point mu(c), for enhancement of color images. We define fuzzy contrast-based quality factor Qf and entropy-based quality factor Qe and the corresponding visual factors for the desired appearance of images. By minimizing the fuzzy entropy of the image information with respect to these quality factors, the parameters t, fh, and mu(c) are calculated globally. By using the proposed technique, a visible improvement in the image quality is observed for under exposed images, as the entropy of the output image is decreased. The terminating criterion is decided by both the visual and quality factors. For over exposed and under plus over exposed images, the proposed fuzzification function needs to be modified by taking maximum intensity as the fourth parameter. The type of the images is indicated by the visual factor which is less than 1 for under exposed images and more than 1 for over exposed images.     

Sparse representation for color image restoration

Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.     

Active learning methods for interactive image retrieval.

Active learning methods have been considered with increased interest in the statistical learning community. Initially developed within a classification framework, a lot of extensions are now being proposed to handle multimedia applications. This paper provides algorithms within a statistical framework to extend active learning for online content-based image retrieval (CBIR). The classification framework is presented with experiments to compare several powerful classification techniques in this information retrieval context. Focusing on interactive methods, active learning strategy is then described. The limitations of this approach for CBIR are emphasized before presenting our new active selection process RETIN. First, as any active method is sensitive to the boundary estimation between classes, the RETIN strategy carries out a boundary correction to make the retrieval process more robust. Second, the criterion of generalization error to optimize the active learning selection is modified to better represent the CBIR objective of database ranking. Third, a batch processing of images is proposed. Our strategy leads to a fast and efficient active learning scheme to retrieve sets of online images (query concept). Experiments on large databases show that the RETIN method performs well in comparison to several other active strategies.     

A robust structure-adaptive hybrid vector filter for color image restoration.

A robust structure-adaptive hybrid vector filter is proposed for digital color image restoration in this paper. At each pixel location, the image vector (i.e., pixel) is first classified into several different signal activity categories by applying a modified quadtree decomposition to luminance component (image) of the input color image. A weight-adaptive vector filtering operation with an optimal window is then activated to achieve the best tradeoff between noise suppression and detail preservation. Through extensive simulation experiments conducted using a wide range of test color images, the filter has demonstrated superior performance to that of a number of well known benchmark techniques, in terms of both standard objective measurements and perceived image quality, in suppressing several distinct types of noise commonly considered in color image restoration, including Gaussian noise, impulse noise, and mixed noise.     

Example-based color transformation of image and video using basic color categories.

Color transformation is the most effective method to improve the mood of an image, because color has a large influence in forming the mood. However, conventional color transformation tools have a tradeoff between the quality of the resultant image and the amount of manual operation. To achieve a more detailed and natural result with less labor, we previously suggested a method that performs an example-based color stylization of images using perceptual color categories. In this paper, we extend this method to make the algorithm more robust and to stylize the colors of video frame sequences. We present a variety of results, arguing that these images and videos convey a different, but coherent mood.     

彩色立体像对的自适应匹配

提出了一种自适应权值的色彩相似性度量方法,有效地抑制了噪声对立体匹配的影响,提出以立体像对左、右图像间的颜色直方图相关系数为标准来调整权值的方法,并通过实验推导出具体的计算公式.对以灰度变化为基准的自适应窗选择方法进行改进,在边界提取过程中加入了彩色信息,使得提取的物体轮廓更为完整;用"米"字型窗口来代替原来的矩形窗口也使得窗口增长更为灵活,获得的匹配更为准确.实验结果表明,本文算法能够生成精确度较高的视差图,是一种较好的立体匹配算法.     

基于协方差描述子的彩色图像分割算法

为克服模糊C均值聚类图像分割对噪声较为敏感的缺陷,提出了一种结合协方差描述子的模糊C均值聚类算法。采用协方差描述子的滤波能力以改善传统模糊C均值聚类算法对噪声敏感的缺陷;提取超像素的协方差矩阵作为特征,降低图像识别的特征冗余。并做了仿真实验,对提出的算法与三个图像分割算法进行比较,结果表明该图像分割算法具有较好的噪声鲁棒性和分割准确率。     

一种彩色图像幸运区域融合算法

幸运区域融合技术通过挑选、配准、拼接短曝光 序列图像中具有一定概率的清晰度 高的“幸运区域”获得一幅高分辨率的重构图像,目前该技术处理对象通常是单色图像。根 据彩色图像成像过程及其数据特点,结合经典的幸运区域融合算法,提出了一种针对彩色短 曝光图像序列的幸运区域融合算法。该算法分别对彩色图像红、绿、蓝3个通道数据进行选 图、配准和拼接,最后合成一幅高分辨率的彩色图像。通过连续拍摄的2200帧短曝光图像, 对所提出的算法进行了多角度验证。红、绿、蓝三通道的选图结果表明,大气湍流确实对光 学场景中不同区域、不同波段光的成像产生随机的干扰,这从一个方面证明红、绿、蓝三通 道分别处理在理论上是成立的。对实测彩色图像融合结果的主观感受和客观指标也证明该算 法可以提高因大气湍流而退化的图像的分辨率。     

确定区域的人脸彩色传递

在对整幅图像色彩传递算法的基础上,提出了确定区域的人脸彩色传递法。首先利用改进的主动形状模型(ASM)方法定位人脸特征点,进而利用区域生长法确定人脸区域。在将参考图像和目标图像(彩色或灰度图)转换到去相关的对立色空间(lαβ颜色空间)后,对于彩色目标图像,分别调整肤色区域图各通道的均值和标准差;对于灰度目标图像,用亮度邻域统计量匹配的方法在参考图像中选取匹配点,并将匹配点的颜色赋值给目标图像中的对应点。最后,把传递结果转换到RGB颜色空间显示。实验结果表明,该方法能有效传递肤色,形成自然逼真的彩色人脸图像。     

基于三角网格上Coons曲面的图像缩放算法

针对传统插值算法在图像整体缩放中边缘区域易产生锯齿或模糊的现象,提出一种新型的图像缩放算法．将相邻像素形成的矩形区域进行三角剖分,根据待构造曲线段的位置关系及插值控制点的距离大小构造辅助的控制顶点,再利用相应的Bézier曲线插值图像构造的辅助控制点,最后以产生的Bézier曲线作为边界曲线,利用Coons曲面构造方法构造出退化的三角形曲面片进行边缘细节的强化。提出的方法可以很好地保持图像边缘细节,尤其在倾斜的边缘和尖角等处保持了更多的细节特征,使放大的图像有更好的效果。     

Adaptive methods for dithering color images

Most color image printing and display devices do not have the capability of reproducing true color images. A common remedy is the use of dithering techniques that take advantage of the lower sensitivity of the eye to spatial resolution and exchange higher color resolution with lower spatial resolution. An adaptive error diffusion method for color images is presented. The error diffusion filter coefficients are updated by a normalized least mean square-type (LMS-type) algorithm to prevent textural contours, color impulses, and color shifts, which are among the most common side effects of the standard dithering algorithms. Another novelty of the new method is its vector character: previous applications of error diffusion have treated the individual color components of an image separately. We develop a general vector approach and demonstrate through simulation studies that superior results are achieved.     

基于颜色直方图的图像检索技术

讨论了基于颜色直方图的图像检索中颜色空间和直方图距离的选择问题,提出了利用颜色直方图来检索图像的一般框架,建立了基于两种颜色空间(RGB和HSV)和四种直方图距离共八种图像检索算法,并且采用了两种客观评价标准对所建立算法的有效性进行评价。结果证明,在基于颜色直方图的应用中,HSV空间比RGB空间更有效,EMD距离在所有四种距离量度中性能较好,但计算量稍大,与EMD距离相比,虽交集距离性能稍有不足,但所需计算量较小。