Inkjet printer model-based halftoning.

The quality of halftone prints produced by inkjet (IJ) printers can be limited by random dot-placement errors. While a large literature addresses model-based halftoning for electrophotographic printers, little work has been done on model-based halftoning for IJ printers. In this paper, we propose model-based approaches to both iterative least-squares halftoning and tone-dependent error diffusion (TDED). The particular approach to iterative least-squares halftoning that we use is direct binary search (DBS). For DBS, we use a stochastic model for the equivalent gray-scale image, based on measured dot statistics of printed IJ halftone patterns. For TDED, we train the tone-dependent weights and thresholds to mimic the spectrum of halftone textures generated by model-based DBS. We do this under a metric that enforces both the correct radially averaged spectral profile and angular symmetry at each radial frequency. Experimental results generated with simulated printers and a real printer show that both IJ model-based DBS and IJ model-based TDED very effectively suppress IJ printer-induced artifacts.     

Adaptive threshold modulation for error diffusion halftoning

Grayscale digital image halftoning quantizes each pixel to one bit. In error diffusion halftoning, the quantization error at each pixel is filtered and fed back to the input in order to diffuse the quantization error among the neighboring grayscale pixels. Error diffusion introduces nonlinear distortion (directional artifacts), linear distortion (sharpening), and additive noise. Threshold modulation, which alters the quantizer input, has been previously used to reduce either directional artifacts or linear distortion. This paper presents an adaptive threshold modulation framework to improve halftone quality by optimizing error diffusion parameters in the least squares sense. The framework models the quantizer implicitly, so a wide variety of quantizers may be used. Based on the framework, we derive adaptive algorithms to optimize 1) edge enhancement halftoning and 2) green noise halftoning. In edge enhancement halftoning, we minimize linear distortion by controlling the sharpening control parameter. We may also break up directional artifacts by replacing the thresholding quantizer with a deterministic bit flipping (DBF) quantizer. For green noise halftoning, we optimize the hysteresis coefficients.     

Algorithms of halftoning color images with edge enhancement

Printers usually generate a limited number of colors and lack the ability of producing continuous-tone color images. Traditional error-diffusion algorithms are used to solve this problem. Compared with other approaches, the approaches of using error-diffusion in general can generate halftoned images of better quality. However, smeared edges and textures may occur in these halftoned images. To produce halftoned images of higher quality, these artifacts due to unstable images, dot-overlap, and error-diffusion must be eliminated or reduced. In this paper, we show that unstable images can be eliminated or reduced through using a proper color difference formula to select the reproduction colors even vector error-diffusion is performed in the RGB domain. We also present a method of using different filters to halftone different components of a color. This approach may have clearer and sharper edges for halftoned color images. Unexpected colors may be generated due to dot-overlap in the printing process. We have presented a method to eliminate this color distortion in the process of error-diffusion. Halftoning a color image by our proposed error-diffusion algorithm with edge enhancement has the following characteristics: the unstable images do not exist; the color-error caused by dot-overlap is corrected; and the smeared edges are sharpened.     

Hierarchical Error Diffusion

This paper develops a distinctive class of color error diffusion algorithm, called hierarchical error diffusion (HED). It aims to achieve perceptually pleasing color halftone through neither conventional joint quantization nor interchannel error diffusion. Instead, it explicitly controls three critical factors sequentially to yield high-quality color halftone: dot-overlapping control, dot-positioning control, and dot-coloring control. A specific implementation of HED is presented with the objective of minimum brightness variation rendering (MBVR). First, an optimal color transform is derived for dot-overlapping control to achieve minimum brightness variation color density (MBVCD). Then, the embedded monochrome error diffusion is employed in dot-positioning control. By sequentially thresholding the elements in partial density sum vector, better dot-positioning is encouraged for more visible color dots. The ldquoblue noiserdquo characteristics of dot-positioning from the monochrome error diffusion are inherited by the color halftone. The simple density priority strategy is applied in dot-coloring control. The pixel color error is diffused channel-independently with a single error filter in halftone dot color space. A comparison with the state-of-the-art color error diffusion algorithms demonstrates excellent halftone quality of HED, while without the typical artifacts of vector error diffusion. Evidence also shows that HED is closer to achieve MBVR than the minimum brightness variation quantization (MBVQ) color diffusion algorithm proposed in.     

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.     

Design and analysis of vector color error diffusion halftoningsystems

Traditional error diffusion halftoning is a high quality method for producing binary images from digital grayscale images. Error diffusion shapes the quantization noise power into the high frequency regions where the human eye is the least sensitive. Error diffusion may be extended to color images by using error filters with matrix-valued coefficients to take into account the correlation among color planes. For vector color error diffusion, we propose three contributions. First, we analyze vector color error diffusion based on a new matrix gain model for the quantizer, which linearizes vector error diffusion. The model predicts the key characteristics of color error diffusion, esp. image sharpening and noise shaping. The proposed model includes linear gain models for the quantizer by Ardalan and Paulos (1987) and by Kite et al. (1997) as special cases. Second, based on our model, we optimize the noise shaping behavior of color error diffusion by designing error filters that are optimum with respect to any given linear spatially-invariant model of the human visual system. Our approach allows the error filter to have matrix-valued coefficients and diffuse quantization error across color channels in an opponent color representation. Thus, the noise is shaped into frequency regions of reduced human color sensitivity. To obtain the optimal filter, we derive a matrix version of the Yule-Walker equations which we solve by using a gradient descent algorithm. Finally, we show that the vector error filter has a parallel implementation as a polyphase filterbank.     

Modeling and quality assessment of halftoning by error diffusion

Digital halftoning quantizes a graylevel image to one bit per pixel. Halftoning by error diffusion reduces local quantization error by filtering the quantization error in a feedback loop. In this paper, we linearize error diffusion algorithms by modeling the quantizer as a linear gain plus additive noise. We confirm the accuracy of the linear model in three independent ways. Using the linear model, we quantify the two primary effects of error diffusion: edge sharpening and noise shaping. For each effect, we develop an objective measure of its impact on the subjective quality of the halftone. Edge sharpening is proportional to the linear gain, and we give a formula to estimate the gain from a given error filter. In quantifying the noise, we modify the input image to compensate for the sharpening distortion and apply a perceptually weighted signal-to-noise ratio to the residual of the halftone and modified input image. We compute the correlation between the residual and the original image to show when the residual can be considered signal independent. We also compute a tonality measure similar to total harmonic distortion. We use the proposed measures for edge sharpening, noise shaping, and tonality to evaluate the quality of error diffusion algorithms.     

Rate-Distortion Coding Simulation for Color Images

We propose a distortion measure for color images, based on a mathematical model of color vision, and supported by subjective image quality evaluations. The visual model is configured after a simplified schematic of the retina's physiology and it transforms the red, green and blue image components into a representation that is consistent with major psychophysical phenomena. The distortion criterion proposed consists of measuring the mean square error in the above representation space. Consistency of the measure with human quality judgment is supported by a subjective ranking experiment, using images distorted in various ways by addition of noise. An optimal coder (in the rate distortion sense) is also simulated, which minimizes the distortion measured as proposed. The resulting image provides a quality standard at the rate distortion bound, against which actual coders can be compared. Finally it is observed that from a statistical point of view, the model's output is a near ideal color image representation for efficient coding.     

A distortion measure for blocking artifacts in images based onhuman visual sensitivity

A visual model that gives a distortion measure for blocking artifacts in images is presented. Given the original and reproduced image as inputs, the model output is a numerical value that quantifies the visibility of blocking error in the reproduced image. The model is derived based on the human visual sensitivity to horizontal and vertical edge artifacts that result from blocking. Psychovisual experiments have been carried out to measure the visual sensitivity to these artifacts. In the experiments, typical edge artifacts are shown to subjects and the sensitivity to them is measured with the variation of background luminance, background activity, edge length, and edge amplitude. Synthetic test patterns are used as background images in the experiments. The sensitivity measures thus obtained are used to estimate the model parameters. The final model is tested on real images, and the results show that the error visibility predicted by the model correlates well with the subjective ranking.     

Perceptual Color Image Coding With JPEG2000

A perceptual color image coder (PCIC) is presented for the $YC_{b}C_{r}$ color space within the framework of JPEG2000. This coder employs a vision model based perceptual distortion metric (PDM) to approximate perceived error for rate-distortion (R-D) optimization in order to maximize the visual quality of coded images. The vision model employed in the PCIC is structurally based on an existing monochromatic multichannel vision model, which is extended for color image coding. Subjective tests with 30 viewers show that the PCIC provides superior picture quality at low to intermediate bitrates in comparison with a JPEG2000 compliant coder employing the mean squared error (MSE) and the visual distortion metric (Cvis) as distortion measures, respectively.      

Attention mechanism enhancement algorithm based on cycle consistent generative adversarial networks for single image dehazing

This paper proposes AMEA-GAN, an attention mechanism enhancement algorithm. It is cycle consistency-based generative adversarial networks for single image dehazing, which follows the mechanism of the human retina and to a great extent guarantees the color authenticity of enhanced images. To address the color distortion and fog artifacts in real-world images caused by most image dehazing methods, we refer to the human visual neurons and use the attention mechanism of similar Horizontal cell and Amazon cell in the retina to improve the structure of the generator adversarial networks. By introducing our proposed attention mechanism, the effect of haze removal becomes more natural without leaving any artifacts, especially in the dense fog area. We also use an improved symmetrical structure of FUNIE-GAN to improve the visual color perception or the color authenticity of the enhanced image and to produce a better visual effect. Experimental results show that our proposed model generates satisfactory results, that is, the output image of AMEA-GAN bears a strong sense of reality. Compared with state-of-the-art methods, AMEA-GAN not only dehazes images taken in daytime scenes but also can enhance images taken in nighttime scenes and even optical remote sensing imagery.     

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.     

Training-based descreening.

Conventional halftoning methods employed in electrophotographic printers tend to produce Moiré artifacts when used for printing images scanned from printed material, such as books and magazines. We present a novel approach for descreening color scanned documents aimed at providing an efficient solution to the Moiré problem in practical imaging devices, including copiers and multifunction printers. The algorithm works by combining two nonlinear image-processing techniques, resolution synthesis-based denoising (RSD), and modified smallest univalue segment assimilating nucleus (SUSAN) filtering. The RSD predictor is based on a stochastic image model whose parameters are optimized beforehand in a separate training procedure. Using the optimized parameters, RSD classifies the local window around the current pixel in the scanned image and applies filters optimized for the selected classes. The output of the RSD predictor is treated as a first-order estimate to the descreened image. The modified SUSAN filter uses the output of RSD for performing an edge-preserving smoothing on the raw scanned data and produces the final output of the descreening algorithm. Our method does not require any knowledge of the screening method, such as the screen frequency or dither matrix coefficients, that produced the printed original. The proposed scheme not only suppresses the Moiré artifacts, but, in addition, can be trained with intrinsic sharpening for deblurring scanned documents. Finally, once optimized for a periodic clustered-dot halftoning method, the same algorithm can be used to inverse halftone scanned images containing stochastic error diffusion halftone noise.     

Color error-diffusion halftoning

Grayscale halftoning converts a continuous-tone image (e.g., 8 bits per pixel) to a lower resolution (e.g., 1 bit per pixel) for printing or display. Grayscale halftoning by error diffusion uses feedback to shape the quantization noise into high frequencies where the human visual system (HVS) is least sensitive. In color halftoning, the application of grayscale error-diffusion methods to the individual colorant planes fails to exploit the HVS response to color noise. Ideally the quantization error must be diffused to frequencies and colors, to which the HVS is least sensitive. Further it is desirable for the color quantization to take place in a perceptual space so that the colorant vector selected as the output color is perceptually closest to the color vector being quantized. This article discusses the design principles of color error diffusion that differentiate it from grayscale error diffusion, focusing on color error diffusion halftoning systems using the red, green, and blue (RGB) space for convenience.     

Model-based halftoning of color images

We present a new class of models for color printers. They form the basis for model-based techniques that exploit the characteristics of the printer and the human visual system to maximize the quality of the printed images. We present two model-based techniques, the modified error diffusion (MED) algorithm and the least-squares model-based (LSMB) algorithm. Both techniques are extensions of the gray-scale model-based techniques and produce images with high spatial resolution and visually pleasant textures. We also examine the use of printer models for designing blue-noise screens. The printer models cam account for a variety of printer characteristics. We propose a specific printer model that accounts for overlap between neighboring dots of ink and the spectral absorption properties of the inks. We show that when we assume a simple "one-minus-RGB" relationship between the red, green, and blue image specification and the corresponding cyan, magenta, and yellow inks, the algorithms are separable. Otherwise, the algorithms are not separable and the modified error diffusion may be unstable, The experimental results consider the separable algorithms that produce high-quality images for applications where the exact colorimetric reproduction of color is not necessary. They are computationally simple and robust to errors in color registration, but the colors are device dependent.     

Demosaicing: image reconstruction from color CCD samples

A simplified color image formation model is used to construct an algorithm for image reconstruction from CCD sensors samples. The proposed method involves two successive steps. The first is motivated by Cok's (1994) template matching technique, while the second step uses steerable inverse diffusion in color. Classical linear signal processing techniques tend to oversmooth the image and result in noticeable color artifacts along edges and sharp features. The question is how should the different color channels support each other to form the best possible reconstruction. Our answer is to let the edges support the color information, and the color channels support the edges, and thereby achieve better perceptual results than those that are bounded by the sampling theoretical limit.     

Fuzzy error diffusion

Quantization errors are generally hidden by performing a dithering operation on the image. A common method is to utilize error diffusion. However, this method is prone to error accumulation, resulting in color impulses and streaks. This paper presents a new approach to error diffusion dithering through a fuzzy error diffusion algorithm. In this method, the amount of error to be diffused is determined by considering the relative location of the pixel not only to the closest codebook vector, but to all other palette entries. The goal is to hide the quantization errors by error diffusion, while preventing the excess accumulation of errors. This is achieved through an attraction-repulsion schema according to a fuzzy membership function. We also explored methods to speed up the fuzzy error diffusion process through a L-filter approach by determining a fixed set of membership values. We have implemented the fuzzy error diffusion algorithm for color images and achieved drastic improvements, resulting in superior quality dithered images and significantly lower mean squared error values. A different error measure modeling the characteristic of the human visual system also indicates the superiority of our method.     

Fuzzy algorithms for combined quantization and dithering

Color quantization reduces the number of the colors in a color image, while the subsequent dithering operation attempts to create the illusion of more colors with this reduced palette. In quantization, the palette is designed to minimize the mean squared error (MSE). However, the dithering that follows enhances the color appearance at the expense of increasing the MSE. We introduce three joint quantization and dithering algorithms to overcome this contradiction. The basic idea is the same in two of the approaches: introducing the dithering error to the quantizer in the training phase. The fuzzy C-means (FCM) and the fuzzy learning vector quantization (FLVQ) algorithms are used to develop two combined mechanisms. In the third algorithm, we minimize an objective function including an inter-cluster separation (ICS) term to obtain a color palette which is more suitable for dithering. The goal is to enlarge the convex hull of the quantization colors to obtain the illusion of more colors after error diffusion. The color contrasts of images are also enhanced with the proposed algorithm. We test the results of these three new algorithms using quality metrics which model the perception of the human visual system and illustrate that substantial improvements are achieved after dithering     

Color image fidelity metrics evaluated using image distortion maps

Several color image fidelity metrics are evaluated by comparing the metric predictions to empirical measurements. Subjects examined image pairs consisting of an original and a reproduction. They marked locations on the reproduction that differed detectably from the original. We refer to the distribution of error marks by the subjects as image distortion maps. The empirically obtained image distortion maps are compared to the predicited visible difference calculated using (1) the widely used root mean square error (point-by-point RMS) computed in uncalibrated RGB values, (2) the point-by-point CIELAB ΔE₉₄ values (CIE, 1994), and (3) S-CIELAB ΔE₉₄, a spatial extension of CIELAB ΔE metric. The uncalibrated RMS metric did not predict the perceptual image distortion data well. The point-by-point CIELAB ΔE₉₄ metric provided better predictions, and the S-CIELAB metric, which incorporated the spatial color sensitivity of the eye, gave the most accurate predictions. None of the metrics provided an excellent fit to the data. Image areas with poor predictions were concentrated in regions containing large negative local contrast. When these areas were excluded from our data analysis, both S-CIELAB and CIELAB predictions had much better agreement with the perceptual data. This suggests that the next step in improving color image fidelity metrics is to redefine color difference formula such as CIELAB ΔE₉₄ in terms of local contrast.     

融合深度学习与多尺度Retinex的水下图像增强方法

针对水下图像纹理模糊和色偏严重等问题,提出了一种融合深度学习与多尺度导向滤波Retinex的水下图像增强方法。首先,将陆上图像采用纹理和直方图匹配法进行退化,构建退化水下图像失真的数据集并训练端到端卷积神经网络(convolutional neural network,CNN) 模型,利用该模型对原始水下图像进行颜色校正,得到色彩复原后的水下图像;然后,对色彩复原图像的亮度通道,采用多尺度Retinex(multi-scale Retinex,MSR) 方法得到纹理增强图像;最后,融合色彩复原图像中的颜色分量和纹理增强图像得到最终水下增强图像。本文利用仿真水下图像数据集和真实水下图像对提出方法进行性能测试。实验结果表明,所提方法的均方根误差、峰值信噪比、CIEDE2000和水下图像质量评价指标分别为0.302 0、17.239 2 dB、16.878 4和4.960 0,优于5种对比方法,增强后的水下图像更加真实自然。本文方法在校正水下图像颜色失真的同时,能有效提升纹理清晰度和对比度。