A simulated annealing restoration algorithm for restoring halftoned color-quantized images

Restoration of color-quantized images is rarely addressed in the literature especially when the images are color-quantized with halftoning. Most existing restoration algorithms are generally inadequate to deal with this problem as they were proposed for restoring noisy blurred images. In this paper, a restoration algorithm based on simulated annealing is proposed to solve the problem. This algorithm makes a good use of the available color palette and the mechanism of a halftoning process to derive useful a priori information for restoration. Simulation results show that it can improve the quality of a halftoned color-quantized image remarkably in terms of both SNR and CIELAB color difference metric. The subjective quality of the restored images can also be improved.     

Look-up table (LUT) method for inverse halftoning

In this paper we propose look-up table (LUT) based methods for inverse halftoning of images. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. The method is extremely fast (no filtering is required) and the PSNR and visual image quality achieved is comparable to the best methods known for inverse halftoning. The LUT inverse halftoning method does not depend on the specific properties of the halftoning method, and can be applied to any halftoning method. Then, an algorithm for template selection for LUT inverse halftoning is introduced. We demonstrate the performance of the LUT inverse halftoning algorithm on error diffused images and ordered dithered images. We also extend LUT inverse halftoning to color halftones.     

A technique for producing scalable color-quantized images with error diffusion.

To reliably and efficiently deliver media information to diverse clients over heterogeneous networks, the media involved must be scalable. In this paper, a color quantization algorithm for generating scalable color-indexed images is proposed based on a multiscale error diffusion framework. Images of lower resolutions are embedded in the outputs such that a simple down-sampling process can extract images of any desirable resolutions. Images possessing this scalable property support transmission over the Internet which contains clients with different display resolutions, systems with different caching resources and networks with varying bandwidths and QoS capabilities. Unlike most of the color halftoning algorithms available nowadays, the proposed algorithm is not dedicated for printing applications but for color-indexed displays. It works with any arbitrary palettes of different size.     

Tree-structured method for LUT inverse halftoning and for imagehalftoning

The authors previously proposed a look up table (LUT) based method for inverse halftoning of images. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. Many of the entries in the LUT are unused because the corresponding binary patterns hardly occur in commonly encountered halftones. These are called nonexistent patterns. In this paper, we propose a tree structure which will reduce the storage requirements of an LUT by avoiding nonexistent patterns. We demonstrate the performance on error diffused images and ordered dither images. Then, we introduce LUT based halftoning and tree-structured LUT (TLUT) halftoning. Even though the TLUT method is more complex than LUT halftoning, it produces better halftones and requires much less storage than LUT halftoning. We demonstrate how the error diffusion characteristics can be achieved with this method. Afterwards, our algorithm is trained on halftones obtained by direct binary search (DBS). The complexity of TLUT halftoning is higher than the error diffusion algorithm but much lower than the DBS algorithm. Also, the halftone quality of TLUT halftoning increases if the size of the TLUT gets bigger. Thus, the halftone image quality between error diffusion and DBS will be achieved depending on the size of the tree-structure in the TLUT algorithm     

Digital color halftoning

Digital color halftoning is the process of transforming continuous-tone color images into images with a limited number of colors. The importance of this process arises from the fact that many color imaging systems use output devices such as color printers and low-bit depth displays that are bilevel or multilevel with a few levels. The goal is to create the perception of a continuous-tone color image using the limited spatiochromatic discrimination capability of the human visual system. In decreasing order of how locally algorithms transform a given image into a halftone and, therefore, in increasing order of computational complexity and halftone quality, monochrome digital halftoning algorithms can be placed in one of three categories: 1) point processes (screening or dithering), 2) neighborhood algorithms (error diffusion), and 3) iterative methods. All three of these algorithm classes can be generalized to digital color halftoning with some modifications. For an in-depth discussion of monochrome halftoning algorithms, the reader is directed to the July 2003 issue of IEEE Signal Processing Magazine. In the remainder of this article, we only address those aspects of halftoning that specifically have to do with color. For a good overview of digital color halftoning, the reader is directed to Haines et al. (2003). In addition, Agar et al. (2003) contains a more in-depth treatment of some of the material found in this work.     

Enhancing inverse halftoning via coupled dictionary training

Inverse halftoning is a challenging problem in image processing. Traditionally, this operation is known to introduce visible distortions into reconstructed images. This paper presents a learning-based method that performs a quality enhancement procedure on images reconstructed using inverse halftoning algorithms. The proposed method is implemented using a coupled dictionary learning algorithm, which is based on a patchwise sparse representation. Specifically, the training is performed using image pairs composed by images restored using an inverse halftoning algorithm and their corresponding originals. The learning model, which is based on a sparse representation of these images, is used to construct two dictionaries. One of these dictionaries represents the original images and the other dictionary represents the distorted images. Using these dictionaries, the method generates images with a smaller number of distortions than what is produced by regular inverse halftone algorithms. Experimental results show that images generated by the proposed method have a high quality, with less chromatic aberrations, blur, and white noise distortions.     

Efficient Total Variation Minimization Methods for Color Image Restoration

In this paper, we consider and study a total variation minimization model for color image restoration. In the proposed model, we use the color total variation minimization scheme to denoise the deblurred color image. An alternating minimization algorithm is employed to solve the proposed total variation minimization problem. We show the convergence of the alternating minimization algorithm and demonstrate that the algorithm is very efficient. Our experimental results show that the quality of restored color images by the proposed method are competitive with the other tested methods.     

On spatial quantization of color images

Image quantization and digital halftoning, two fundamental image processing problems, are generally performed sequentially and, in most cases, independent of each other. Color reduction with a pixel-wise defined distortion measure and the halftoning process with its local averaging neighborhood typically optimize different quality criteria or, frequently, follow a heuristic approach without reference to any quantitative quality measure. In this paper, we propose a new model to simultaneously quantize and halftone color images. The method is based on a rigorous cost-function approach which optimizes a quality criterion derived from a simplified model of human perception. It incorporates spatial and contextual information into the quantization and thus overcomes the artificial separation of quantization and halftoning. Optimization is performed by an efficient multiscale procedure which substantially alleviates the computational burden. The quality criterion and the optimization algorithms are evaluated on a representative set of artificial and real-world images showing a significant image quality improvement compared to standard color reduction approaches. Applying the developed cost function, we also suggest a new distortion measure for evaluating the overall quality of color reduction schemes.     

颜色迁移算法在古建筑虚拟色彩修复中的应用

分析了彩色图像间色彩传递算法和灰度图像彩色化算法的优缺点和应用特点,描述了两种典型颜色迁移算法在古建筑虚拟色彩修复中的应用,实验结果表明颜色迁移算法可成为古建筑虚拟色彩修复的有效工具.     

Inverse halftoning algorithm using edge-based lookup table approach

The inverse halftoning algorithm is used to reconstruct a gray image from an input halftone image. Based on the recently published lookup table (LUT) technique, this paper presents a novel edge-based LUT method for inverse halftoning which improves the quality of the reconstructed gray image. The proposed method first uses the LUT-based inverse halftoning method as a preprocessing step to transform the given halftone image to a base gray image, and then the edges are extracted and classified from the base gray image. According to these classified edges, a novel edge-based LUT is built up to reconstruct the gray image. Based on a set of 30 real training images with both low- and high-frequency contents, experimental results demonstrated that the proposed method achieves a better image quality when compared to the currently published two methods, by Chang et al. and Mes$80e and Vaidyanathan.     

A new optimal digital halftoning technique based on the discrete cosine transform

Digital halftoning is a technique to display a gray-level image with a bilevel device. Conventionally, most halftoning techniques are done in the spatial domain. A new halftoning technique based on the discrete cosine transform is proposed. The method chooses an optimal bilevel image to display the original gray-level image and minimize the weighted mean square error based on the discrete cosine transform domain. The simulation results indicate that our algorithm can produce very good halftoned images without false contours.     

一种用于水下图像的光谱重构方法:理论及应用

多光谱成像是一项非常有前景的图像高保真获取与再现技术,近年来在水下物体颜色还原的应用中也受到的极大的需求和关注。然而,不同于空气中的物体的成像过程,在水下成像过程中,当光通过水而进行传播,光被水体严重吸收和散射,导致图像变暗,在其光谱和颜色方面发生模糊和扭曲。文中讨论的是基于水下图像的水衰减系数的校准和其多光谱图像的光谱重构。首先在不同的距离处获取物体的图像,提出了基于不同距离的图像进行水体衰减系数的校准并恢复原始图像的技术;在此基础上,分析并导出满足系数校准和图像复原所需的在不同距离获取到的最少的原始图像个数。最后,通过比较复原的水下图像与空气中获取的彩色图像,实验结果证明:文中提出的技术能够对水下光谱图像的进行精确颜色复原,所有测试图像的平均相对残留误差仅为5.87％。     

High-capacity data hiding in halftone images using minimal-error bit searching and least-mean square filter.

In this paper, a high-capacity data hiding is proposed for embedding a large amount of information into halftone images. The embedded watermark can be distributed into several error-diffused images with the proposed minimal-error bit-searching technique (MEBS). The method can also be generalized to self-decoding mode with dot diffusion or color halftone images. From the experiments, the embedded capacity from 33% up to 50% and good quality results are achieved. Furthermore, the proposed MEBS method is also extended for robust watermarking against the degradation from printing-and-scanning and several kinds of distortions. Finally, a least-mean square-based halftoning is developed to produce an edge-enhanced halftone image, and the technique also cooperates with MEBS for all the applications described above, including high-capacity data hiding with secret sharing or self-decoding mode, as well as robust watermarking. The results prove much sharper than the error diffusion or dot diffusion methods.     

基于显著性加权随机优化的快速响应码美化方法

随着移动终端和移动互联网的发展,快速响应(QR)码被广泛运用于移动信息交互。但是,标准的 QR 码是由均匀分布的黑色与白色模块组成,外观类似噪声信号,缺乏视觉美感,限制了QR码的应用。针对此问题,该文提出一种提高QR码视觉效果的美化方法。该方法将给定的彩色图像进行显著性检测和半色调处理,得到相应的显著性矩阵和半色调图像,然后根据半色调图像上的网点分布,对QR码的模块分布进行优化。为了提高优化效率,该文提出一种显著性加权随机优化算法,将优化后的QR码和半色调图像进行融合,得到与半色调图像最相似的半色调QR码。在图像渲染阶段,该文提出一种基于二分搜索的色彩调整算法,得到具有色彩信息和视觉美感的彩色QR码。实验分析表明,该方法生成的QR码不仅保留了与标准QR码一样的抗遮挡、快速解码等特性,还有效地提升了视觉效果,具有很好的视觉吸引力。     

单幅图像的快速去雾算法

雾的存在使得户外图像的处理变得困难。雾、霭、烟等现象会使彩色图像退化,对比度降低。介绍了一种单幅图像的去雾新算法,不需要分割图像,直接利用高斯低通滤波器分离出背景空气光,利用改良的暗通道法对大气光进行估计,结合雾天图像的物理模型对图像进行复原,最后再对图像的饱和度进行校正,得到最终的复原效果。该算法的主要优点是速度快,...     

Model-based color halftoning using direct binary search.

In this paper, we develop a model-based color halftoning method using the direct binary search (DBS) algorithm. Our method strives to minimize the perceived error between the continuous tone original color image and the color halftone image. We exploit the differences in how the human viewers respond to luminance and chrominance information and use the total squared error in a luminance/chrominance based space as our metric. Starting with an initial halftone, we minimize this error metric using the DBS algorithm. Our method also incorporates a measurement based color printer dot interaction model to prevent the artifacts due to dot overlap and to improve color texture quality. We calibrate our halftoning algorithm to ensure accurate colorant distributions in resulting halftones. We present the color halftones which demonstrate the efficacy of our method.     

基于离散Sibson插值的反半色调算法

本文提出了一种基于离散Sibson插值的盲反半色调方法.算法建立在统一的离散Voronoi图框架之上,通过自适应Voronoi区域估计得到种子点的灰度值,然后通过离散Sibson插值将传统的反半色调问题转化为插值问题并计算获取连续色调图像.算法不需要知道任何先验知识,因此算法具有更广的适用性,可以被应用于任意方法生成的...     

POCS-based algorithm for restoring colour-quantised images

The paper studies the restoration of colour-quantised images. Restoration of colour-quantised images is rarely addressed in the literature, and direct applications of existing restoration techniques are generally inadequate to deal with this problem. The authors propose a POCS-based restoration algorithm specific to colour-quantised images, which makes a good use of the available colour palette to derive useful a priori information for restoration. The proposed restoration algorithm is shown to be capable of improving the quality of a colour-quantised image to a certain extent.     

基于颜色失真去除与暗通道先验的水下图像复原

水下图像成像过程与雾天图像虽然类似,但因水对光的选择性吸收和光的散射作用,水下图像存在颜色衰减并呈现蓝(绿)色基调,传统的去雾方法用于水下图像复原时效果欠佳。针对这类方法出现的缺点,该文根据先去除颜色失真后去除背景散射的思路,提出一种新的水下图像复原方法。结合光在水中的衰减特性,提出适用于水下图像的颜色失真去除方法,并利用散射系数与波长的关系修正各通道透射率;另外,该文改进的背景光估计方法可有效避免人工光源、白色物体、噪声等影响。实验结果证明,该文方法在恢复场景物体原本颜色和去除背景散射方面效果良好。     

Digital color halftoning with generalized error diffusion andmultichannel green-noise masks

In this paper, we introduce two novel techniques for digital color halftoning with green-noise-stochastic dither patterns generated by homogeneously distributing minority pixel clusters. The first technique employs error diffusion with output-dependent feedback where, unlike monochrome image halftoning, an interference term is added such that the overlapping of pixels of different colors can be regulated for increased color control. The second technique uses a green-noise mask, a dither array designed to create green-noise halftone patterns, which has been constructed to also regulate the overlapping of different colored pixels. As is the case with monochrome image halftoning, both techniques are tunable, allowing for large clusters in printers with high dot-gain characteristics, and small clusters in printers with low dot-gain characteristics.