Color Artifacts Research Articles

Color Filter Array Demosaicking Based on the Distribution of Directional Color Differences

Color artifacts are often found at edges due to interpolation across them. To tackle this problem, four high-order interpolants for four different directions are determined, and a weight based on the corresponding absolute directional color differences is assigned to each of them. In a smooth area, the interpolants in the four directions will have equal normalized weights of 1/4. In a nonsmooth area with a sharp edge, the normalized weights for the interpolants on the opposite side of an edge will be small and less than 1/4, but this inclusion will still manifest itself as color artifacts in the output image. In our proposed method, the frequency distribution of the weights for the entire image is first determined. To reduce color artifacts, the weights are hard thresholded such that any weights below a threshold are set to zero, and that optimal threshold is determined according to the frequency distribution of the directional color differences. A detail-preserving median based multishell filter is then applied to the fully demosaicked green channel to prune outliers resulting from high-order interpolation of thin features. Based on the experimental results, it has been shown that our proposed algorithm outperforms the other algorithms with minimal color artifacts for various types of images.

69‐3: New Multiplexing Method for Quasi Static and Artefact Free Color LED Matrix Displays

Multiplex drive is generally used for electronic displays with many pixel elements to reduce the cost. For RGB LED displays spatial multiplexing or color multiplexing methods are generally used even if high currents are generally required and may cause electromagnetic interferences. In addition color artefacts are always present when taking pictures or videos. In the present paper we present a new spatial/color multiplying method that reduces all these drawbacks.

Time-Sequential Working Wavelength-Selective Filter for Flat Autostereoscopic Displays

A time-sequential working, spatially-multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array and a fast color display is presented. The newly-introduced 3D display design is usable as a multi-user display, as well as a single-user system. The wavelength-selective filter barrier emits the light from a larger aperture than common autostereoscopic barrier displays with similar barrier pitch and ascent. Measurements on a demonstrator with commercial display components, simulations and computational evaluations have been carried out to describe the proposed wavelength-selective display design in static states and to show the weak spots of display filters in commercial displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. In the time-sequential implementation, it is important to avoid that quick eye or eyelid movement leads to visible color artifacts. Therefore, color filter cells, switching faster than conventional LC display cells, must distribute directed light from different primaries at the same time, to create a 3D presentation. For that, electric tunable liquid crystal Fabry–Pérot color filters are presented. They switch on-off the colors red, green and blue in the millisecond regime. Their active areas consist of a sub-micrometer-thick nematic layer sandwiched between dielectric mirrors and indium tin oxide (ITO)-electrodes. These cells shall switch narrowband light of red, green or blue. A barrier filter array for a high resolution, glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures.

Gamut Extension for Cinema.

Emerging display technologies are able to produce images with a much wider color gamut than those of conventional distribution gamuts for cinema and TV, creating an opportunity for the development of gamut extension algorithms (GEAs) that exploit the full color potential of these new systems. In this paper, we present a novel GEA, implemented as a PDE-based optimization procedure related to visual perception models, that performs gamut extension (GE) by taking into account the analysis of distortions in hue, chroma, and saturation. User studies performed using a digital cinema projector under cinematic (low ambient light, large screen) conditions show that the proposed algorithm outperforms the state of the art, producing gamut extended images that are perceptually more faithful to the wide-gamut ground truth, as well as free of color artifacts and hue shifts. We also show how currently available image quality metrics, when applied to the GE problem, provide results that do not correlate with users' choices.

Novel quality-efficient universal demosaicing for arbitrary color filter array images

To save cost, nowadays most digital cameras equip with a single sensor covered with a specific color filter array (CFA) and each pixel in the captured CFA image is composed of only one color. Universal demosaicing is the process to reconstruct the full-RGB (red-green-blue) color image through an input arbitrary CFA image. In this paper, we propose a novel quality-efficient universal demosaicing method which exploits both the edge information and the color correlation during demosaicing and can work for arbitrary RGB and non-RGB CFA images. In the proposed method, two main contributions are made and they are (1) an effective universal edge-directed color difference based interpolation is designed to specially demosaic the edge regions which usually appear severe artifacts by using the existing universal demosaicing methods and (2) a new Fourier transform-based technique combined with the Sobel edge detection is developed to derive the masks for extracting gradient information from arbitrary CFA images so as to provide better weights in the proposed universal edge-directed interpolation, leading to better quality of the demosaiced images. Based on the 24 typical testing images from Kodak collection, experimental results showed that the proposed universal demosaicing method possesses the quality advantage in terms of average color peak signal-to-noise ratio (CPSNR) and S-CIELAB and demonstrates less color artifacts in terms of visual perception when compared with the three state-of-the-art universal demosaicing methods and some existing demosaicing methods designed for non-RGB CFA images.

Compensating for color artifacts in the design of technical kaleidoscopes

In the applications of computer graphics, bidirectional texture function (BTF) is used for realistic and predictive rendering. The goal of current research is to get a surface representation indistinguishable from the real world. We developed, built, and tested a portable instrument for BTF acquisition based on kaleidoscopic imaging. We discuss the color issues we experienced after the initial tests. We show that the same color balance cannot be applied to the whole image as the spectral response of the instrument varies depending on the position within the image. All optical elements were inspected for their contributions to the spectral behavior of the instrument. A software simulator of a mathematical model of the device was implemented. We found a way to implement all these contributions into the image processing pipeline. In this way, the correct white balance for each individual pixel in the image is found and applied, allowing for a more faithful color representation. Also proposed is an optimized dielectric protective layer for the kaleidoscope’s mirrors causing the least possible color aberration.

A spatially variant total variational model for chromatic aberration correction

In this paper, we propose a spatially variant total variational model to correct chromatic aberration (CA) that causes false color artifacts near edges in captured images. In general, it may be very difficult to determine suitably CA regions in captured images. Instead of using local image processing methods, our idea is to make use of spatially variant model to control the gradient and intensity matching between the red and blue color channels and the green color channel at the edges. The total variation regularization is also employed to constraint the change of the intensity of red and blue color channels during the gradient and intensity matching. We present both theoretical results and algorithms for the proposed model. Experimental results are given to illustrate the effectiveness of the proposed model and algorithm and show that their corrected images are visually better than those corrected by the other testing methods.

Color-Line Regularization for Color Artifact Removal

Existing regularization functions for color image restoration cannot remove color artifact, undesirable appearance of colors, sufficiently, which degrades the quality of color images. In this paper, we propose a new regularization function, named the local color nuclear norm (LCNN), for removing color artifact in color image restoration. LCNN is designed to promote the local color-line property: the color distribution of each local region of a color image exhibits strong linearity. The local color-line property is inherent in clean color images and is violated by color artifact, so that suppressing LCNN is expected to reduce color artifact effectively. In addition, the very nature of LCNN allows us to incorporate it into various color image restoration formulations, where the associated optimization problems can be efficiently solved by proximal splitting techniques. Several illustrative applications of LCNN are presented with comprehensive experimental results.

A spatiotemporal restoration of partial color artifacts in archival films

With the rapid growth in video technology and marketability of all forms of visual media, we encounter the need to exploit archive material more effectively and guarantee picture quality. Old films and video often get corrupted by many artifacts due to extensive usage or improper storage. These defects not only lead to reduction in perceptual quality, but also require large transmission bandwidth. In this paper, we present a novel method to restore color image sequences corrupted by partial color artifact. This degradation visible in old color film/video appear as random color patches in the frames. It reduces the perceptual quality of the video. The proposed approach minimizes a convex function, formulated as rank minimization problem, seeking the temporal correlation among the frames in a video sequence. The main feature of the proposed algorithm is its accurate detection and fast restoration of the artifacts present in a video shot.

Content-Dependent Reduction of Static Color Breakup on Field Sequential Color LCDs

Field sequential color (FSC) LCDs form color images by sequentially displaying subfield images. Because color filter attenuating light transmission is not required, FSC LCDs can reduce power consumption considerably. However, their sequential display scheme results in undesirable color artifacts that degrade the perceived image quality. This paper proposes a new content-dependent static color breakup (CBU) reduction method for FSC LCDs with 240-Hz frame rate. In addition, two performance indices are defined to examine the effectiveness of the backlight color selections. Experimental results indicate that the proposed method effectively reduces the visibility of static CBU.

Demosaiced pixel super-resolution for multiplexed holographic color imaging.

To synthesize a holographic color image, one can sequentially take three holograms at different wavelengths, e.g., at red (R), green (G) and blue (B) parts of the spectrum, and digitally merge them. To speed up the imaging process by a factor of three, a Bayer color sensor-chip can also be used to demultiplex three wavelengths that simultaneously illuminate the sample and digitally retrieve individual set of holograms using the known transmission spectra of the Bayer color filters. However, because the pixels of different channels (R, G, B) on a Bayer color sensor are not at the same physical location, conventional demosaicing techniques generate color artifacts in holographic imaging using simultaneous multi-wavelength illumination. Here we demonstrate that pixel super-resolution can be merged into the color de-multiplexing process to significantly suppress the artifacts in wavelength-multiplexed holographic color imaging. This new approach, termed Demosaiced Pixel Super-Resolution (D-PSR), generates color images that are similar in performance to sequential illumination at three wavelengths, and therefore improves the speed of holographic color imaging by 3-fold. D-PSR method is broadly applicable to holographic microscopy applications, where high-resolution imaging and multi-wavelength illumination are desired.

Color calibration and fusion of lens-free and mobile-phone microscopy images for high-resolution and accurate color reproduction.

Lens-free holographic microscopy can achieve wide-field imaging in a cost-effective and field-portable setup, making it a promising technique for point-of-care and telepathology applications. However, due to relatively narrow-band sources used in holographic microscopy, conventional colorization methods that use images reconstructed at discrete wavelengths, corresponding to e.g., red (R), green (G) and blue (B) channels, are subject to color artifacts. Furthermore, these existing RGB colorization methods do not match the chromatic perception of human vision. Here we present a high-color-fidelity and high-resolution imaging method, termed “digital color fusion microscopy” (DCFM), which fuses a holographic image acquired at a single wavelength with a color-calibrated image taken by a low-magnification lens-based microscope using a wavelet transform-based colorization method. We demonstrate accurate color reproduction of DCFM by imaging stained tissue sections. In particular we show that a lens-free holographic microscope in combination with a cost-effective mobile-phone-based microscope can generate color images of specimens, performing very close to a high numerical-aperture (NA) benchtop microscope that is corrected for color distortions and chromatic aberrations, also matching the chromatic response of human vision. This method can be useful for wide-field imaging needs in telepathology applications and in resource-limited settings, where whole-slide scanning microscopy systems are not available.

Tri-linear color multi-linescan sensor with 200 kHz line rate

In this paper we present a newly developed linear CMOS high-speed line-scanning sensor realized in a 0.35μm CMOS OPTO process for line-scan with 200kHz true RGB and 600kHz monochrome line rate, respectively. In total, 60 lines are integrated in the sensor allowing for electronic position adjustment. The lines are read out in rolling shutter manner. The high readout speed is achieved by a column-wise organization of the readout chain. At full speed, the sensor provides RGB color images with a spatial resolution down to 50μm. This feature enables a variety of applications like quality assurance in print inspection, real-time surveillance of railroad tracks, in-line monitoring in flat panel fabrication lines and many more. The sensor has a fill-factor close to 100%, preventing aliasing and color artefacts. Hence the tri-linear technology is robust against aliasing ensuring better inspection quality and thus less waste in production lines.

Color Artifact-Free Three-Dimensional Integral Imaging Display with Large Depth Using a Lens Array Based on Sublens Structure

Typical depth priority integral imaging (DPII) method with large depth is composed of a lens array and a display panel where the unwanted color artifact of the reconstructed 3D image may be produced due to the subpixel structure of the display panel. In this paper, to reduce color artifact in the DPII system, we propose a new DPII system with a lens array based on three sublens configurations. In the proposed system, each voxel color of 3D object is reconstructed by the integration of the subpixel images from three sublenses to display red, green, and blue colors. In addition, we introduce a new subpixel rearrangement algorithm to generate the modified elemental images for the proposed system. To show the feasibility of the proposed method, we carry out the preliminary experiments for color images and present the experimental results. The results reveal that the proposed system can reduce color artifact of 3D images compared with the conventional integral imaging system.

Probabilistic Gaussian similarity‐based local colour transfer

A simple and robust probabilistic colour transfer method using feature matching and Gaussian mixture model (GMM)-based soft segmentation is proposed. Traditional colour transfer methods using GMM colour model require proper Gaussian matching where the euclidean distances of mean colours and positions of spatially overlapping regions are usually used as a matching measure. It is, however, difficult for these methods to find proper Gaussian matches for images taken from different illuminations, brightnesses, view-points as well as different camera types and settings. This is troublesome since incorrect matches result in unwanted colour artefacts. To cope with this problem, a new Gaussian similarity measure and probabilistic colour transfer formulation are presented. Experimental results demonstrate that, compared with previous methods, more robust and proper colour transfer results are generated for images taken from different conditions.

On the Implementation of Collaborative TV Regularization: Application to Cartoon+Texture Decomposition

This paper deals with the analysis, implementation, and comparison of several vector-valued total variation (TV) methods that extend the Rudin-Osher-Fatemi variational model to color images. By considering the discrete gradient of a multichannel image as a 3D structure matrix with dimensions corresponding to the spatial extend, the differences to other pixels and the color channels, we introduce in [J. Duran, M. Moeller, C. Sbert, and D. Cremers, 'Collaborative Total Variation: A General Framework for Vectorial TV Models', SIAM Journal on Imaging Sciences, 9(1), pp.116-151, 2016] collaborative sparsity enforcing norms for penalizing the resulting tensor. We call this class of regularizations collaborative total variation (CTV). We first analyze the denoising properties of each collaborative norm for suppressing color artifacts while preserving image features and aligning edges. We then describe the primal-dual hybrid gradient method for solving the minimization problem in detail. The resulting CTV–L2 variational model can successfully be applied to many image processing tasks. On the one hand, an extensive performance comparison of several collaborative norms for color image denoising is provided. On the other hand, we analyze the ability of different CTV methods for decomposing a multichannel image into a cartoon and a textural part. Finally, we also include a short discussion on alternative minimization methods and compare their computational efficiency.

No-Reference Perceived Image Quality Algorithm for Demosaiced Images

Visual image quality assessment (IQA) plays a key role in every multimedia application, as end user to it is a human-being. Real time applications demand no reference (NR) IQA, due to unavailability of the reference image. Today, most of the perceived/visual NR-IQA algorithms developed are for distortions like blur, ringing, and blocking artifacts. Very few are available for color distortions. Visible color distortions, such as false color, and zipper are produced in the demosaiced image due to incorrect interpolation of missing color values. In this paper, state of the art zipper and false color artifact quantification algorithms, general purpose NR-IQA algorithms are evaluated for visual quality assessment of demosaiced images. Separate NR- IQA algorithms are proposed for zipper and false color artifact quantification these scores are then combined to obtain final quality score for demosaiced image. Zipper algorithm quantifies zipper artifact by searching for zipper pixels in an image. While, false color algorithm finds correlation between local high frequency region’s color planes to quantify false color.

Collaborative Total Variation: A General Framework for Vectorial TV Models

Even after over two decades, the total variation (TV) remains one of the most popular regularizations for image processing problems and has sparked a tremendous amount of research, particularly to move from scalar to vector-valued functions. In this paper, we consider the gradient of a color image as a three dimensional matrix or tensor with dimensions corresponding to the spatial extend, the differences to other pixels, and the spectral channels. The smoothness of this tensor is then measured by taking different norms along the different dimensions. Depending on the type of these norms one obtains very different properties of the regularization, leading to novel models for color images. We call this class of regularizations collaborative total variation (CTV). On the theoretical side, we characterize the dual norm, the subdifferential and the proximal mapping of the proposed regularizers. We further prove, with the help of the generalized concept of singular vectors, that an $\ell^{\infty}$ channel coupling makes the most prior assumptions and has the greatest potential to reduce color artifacts. Our practical contributions consist of an extensive experimental section where we compare the performance of a large number of collaborative TV methods for inverse problems like denoising, deblurring and inpainting.

A Review of Various Transform Domain Digital Image Fusion for Multifocus Colored Images

Image fusion is the idea to enhance the image content by fusing two or more images obtained from visual sensor network. The main goal of image fusion is to eliminate redundant information and merging more useful information from source images. Various transform domain image fusion methods like DWT, SIDWT and DCT, ACMax DCT etc. are developed in recent years. Every method has its own advantages and disadvantages. ACMax Discrete cosine transform (DCT) is very efficient approach for image fusion because of its energy compaction property as well as improve quality of image. Furthermore, this technique has also some disadvantages like color artifacts, noise and degrade the sharpness of edges. In this paper ACMax DCT method is integrated with saturation weighting and Joint Trilateral filter to get the high quality image and compare with traditional methods. The results have shown that ACMax DCT with Saturation weighting and Joint Trilateral filter method has outperformed the state of art techniques.

Color Filter Array Demosaicking by Using Subband Synthesis Scheme

Digital still cameras use image sensors to capture images. To reduce the size of digital still cameras, a single image sensor instead of three image sensors should be used. A single image sensor overlaid with a color filter array (CFA) that filters color channels only captures one color channel at each pixel; thus, the other missing color channels at each pixel must be reproduced from neighboring pixels. This procedure is known as CFA interpolation or demosaicking. In this paper, a novel CFA interpolation method is proposed to reduce false color artifacts and improve the quality of restored images. First, an edge detection method is used to interpolate the initial missing color channels. Second, the high correlations between wavelet subbands of the different color channels are explored to obtain accurate color values of the estimated green channel. Finally, the high-frequency subbands of red and blue channels are iteratively updated to reduce false color artifacts. Experimental results show that our proposed method preserves edges and restores images with high quality.