Color Artifacts Research Articles

Robust, Error-Tolerant Photometric Projector Compensation.

We propose a novel error tolerant optimization approach to generate a high-quality photometric compensated projection. The application of a non-linear color mapping function does not require radiometric pre-calibration of cameras or projectors. This characteristic improves the compensation quality compared with related linear methods if this approach is used with devices that apply complex color processing, such as single-chip digital light processing projectors. Our approach consists of a sparse sampling of the projector's color gamut and non-linear scattered data interpolation to generate the per-pixel mapping from the projector to camera colors in real time. To avoid out-of-gamut artifacts, the input image's luminance is automatically adjusted locally in an optional offline optimization step that maximizes the achievable contrast while preserving smooth input gradients without significant clipping errors. To minimize the appearance of color artifacts at high-frequency reflectance changes of the surface due to usually unavoidable slight projector vibrations and movement (drift), we show that a drift measurement and analysis step, when combined with per-pixel compensation image optimization, significantly decreases the visibility of such artifacts.

An edge-adaptive demosaicking method based on image correlation

To reduce the cost, size and complexity, a consumer digital camera usually uses a single sensor overlaid with a color filter array (CFA) to sample one of the red-green-blue primary color values, and uses demosaicking algorithm to estimate the missing color values at each pixel. A novel image correlation and support vector machine (SVM) based edge-adaptive algorithm was proposed, which can reduce edge artifacts and false color artifacts, effectively. Firstly, image pixels were separated into edge region and smooth region with an edge detection algorithm. Then, a hybrid approach switching between a simple demosaicking algorithm on the smooth region and SVM based demosaicking algorithm on the edge region was performed. Image spatial and spectral correlations were employed to create middle planes for the interpolation. Experimental result shows that the proposed approach produced visually pleasing full-color result images and obtained higher CPSNR and smaller S-CIELAB ΔE*ab than other conventional demosaicking algorithms.

Contrast Enhancement of an Image using Fuzzy Logic

enhancement plays a significant role in vision applications. Many techniques have been proposed so far for enhancing the images. It has been found that the most of the existing techniques are based upon the transform domain methods; which may introduce the color artefacts and also may reduce the intensity of the input remote sensing image. To overcome this problem a modified approach is introduced in this research work. The new integrated approach has the capability to enhance the contrast in digital images in efficient manner by using the modified fuzzy based enhancement algorithm. Modified fuzzy image enhancement has integrated image gradients with input image for image enhancement. After image enhancement using modified fuzzy based algorithms the color normalization has come in action to reduce color artefacts. In order to evaluate the significant improvement of the proposed various well known images has been selected for experimental results. The experimental results have shown that the proposed technique has quite effective improvement over the available techniques.

Performance Evaluation of Fuzzy and Histogram Based Color Image Enhancement

Abstract In computer vision applications, image enhancement plays an important role. It has been analyzed that the majority of the existing enhancement techniques are in relation to the transform domain methods, which can introduce the color artifacts and also may reduce steadily the intensity of the input image. To overcome this dilemma, a fuzzy based algorithm has been used. This approach could have the ability to boost the contrast in digital images in an efficient manner by utilizing the histogram based fuzzy image enhancement algorithm. The overall objective of this paper is to evaluate the effectiveness of histogram and fuzzy based image en-hancement for various kinds of images like underwater, remote sensing, medical etc. The fuzzy and histogram based enhancement has been designed and implemented in MATLAB using image processing toolbox. The result has shown the effectiveness of the fuzzy based enhancement over the existing techniques

Denoising of colour images using window contrast enhancement and vector alignment

In this paper a two stage filtering scheme for removal of random valued impulse noise from colour images is presented. The first stage consisting of an impulse detector employs iterative contrast enhancement in the filtering window. The performance of the impulse detector is further improved by exploiting the inherent correlation among three colour components. The second stage restores the noisy pixel by optimally aligning the pixel vector such that the brightness and the colour of the restored pixel are close to the original pixel. As the proposed method also includes the noise free component in the process of restoration of noisy components, unlike other component-wise filtering approaches where restoration of noisy components is done independently, colour artefacts in the present scheme are avoided. A significantly improved performance of the proposed detection and filtering approach is demonstrated through several experiments.

Enriched Digital Image Amalgamation Using Alternating Current Coefficients in DCT Domain

The main objective of image fusion is to combine information from multiple images of the same scene in order to deliver only the useful information. The DCT based methods of image fusion are more suitable and time-saving in real-time systems using DCT based standards of still images. In this paper an efficient approach for fusion of multi-focus images based on variance calculated in DCT domain has been presented. This research work has proposed a new technique which has integrated the higher valued Alternating Current coefficients calculated in DCT domain based fusion with color normalization with adaptive histogram equalization to reduce the color artifacts which will be introduced due to the transform domain method i.e. DCT. The fusion process may degrades the sharpness of the edges in the digital images so to overcome this problem adaptive histogram equalization has also been integrated with the proposed algorithm to enhance the results further.

UDP – a New Universal Demosaicing Pipeline Algorithm for RGB Color Filter Arrays

Abstract A new universal demosaicing method suitable for imaging pipelines using a RGB color filter array (CFA) is proposed. The proposed algorithm can reconstruct single-sensor images registered by a digital camera equipped with any RGB CFA currently in use. The algorithm includes a spatio-spectral interpolation model, a new edge-detection technique, and a post processing to reduce the color shifts and artifacts of the reconstructed image. Experimental results over various test CFA patterns and real images demonstrate the efficiency and feasibility of a universal demosaicing algorithm proposed in the paper. Two methods - objective metrics (mean squared error - MSE and peak signal to noise ratio - PSNR), and subjective evaluation are used to analyze the results

An Efficient Super Resolution Based on Image Dimensionality Reduction Using Accumulative Intensity Gradient

An efficient super resolution algorithm based on edge direction is proposed based on the dimensional reduction of color images and 3 types of edge direction. The basic idea is to reduce image – especially color image – dimensions and to interpolate pixels by using 3 simple edge directions – vertical, horizontal, and diagonal. The proposed algorithm conceivably eliminate more color artifacts than Bicubic. The results of experiments using 30 natural images confirm that PSNR of the proposed method achieve the same quality as Fast Curvature Based Interpolation (FCBI). We confirmed that computation time for the proposed method was 40% shorter for RGB and 20% shorter for grayscale images than the previous fastest method, i.e., FCBI. We show efficient panorama image generation based on the proposed super resolution method as one application of our proposal.

An integrated spectral imaging system for producing color images of static and moving objects

AbstractAn integrated spectral imaging system constructed by synchronizing a programmable light source, a high‐speed monochrome camera, and a display device is proposed to produce tristimulus images of static and moving objects effectively in real time onto the display. This system is called the CIE‐XYZ display. Active spectral illuminants, containing both the device characteristics of camera and display, are projected onto object surfaces as time sequence. The images are captured synchronously by the camera and quickly transmitted to the display device in the RGB signal form so that the accurate tristimulus images are displayed. First, we describe the principle of the CIE‐XYZ tristimulus display. The theoretically optimal illuminants contain negative parts in the spectral curves. Second, we design practical illuminants with all positive spectral curves. The color images in our system are composed of the time sequence of RGB component images. Then, the synthesized color images on the display contain color artifacts when objects move fast. An image processing algorithm for correcting the motion color artifact is proposed based on optical flow estimation using a graphics processing unit. The comprehensive performance of the proposed system and algorithms is examined using both static and moving objects. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 329–340, 2015

Implementation of Nonlocal Pansharpening Image Fusion

This paper focuses on the implementation of the pansharpened image fusion technique proposed in the companion paper [A Nonlocal Variational Model for Pansharpening Image Fusion, SIAM Journal on Imaging Sciences, 2014, to appear]. Pansharpening refers to the process of inferring a high resolution multispectral image from a high resolution panchromatic image and low resolution multispectral one. Although quite successful in terms of relative error, state-of-the-art pansharpening methods still introduce relevant color artifacts. The variational pansharpening model proposed by Buades et al. incorporates a nonlocal regularization term that takes advantage of image self-similarity, leading to signicant reduction of the above-mentioned color artifacts.

The diagnostic value of the twinkle sign in color Doppler imaging of urinary stones

ObjectiveTo determine the diagnostic value of the color Doppler twinkle sign in the diagnosis of urinary stones. MethodsThe study examined 100 urinary stones in 71 patients who presented with urinary symptoms and were suspected to have urinary stones. Patients were examined by gray scale and color Doppler ultrasound as well as non-contrast spiral computed tomography (CT), with CT used as the reference standard. The twinkle sign was identified as a color artifact associated with urinary stones, presenting as rapidly changing colors seen posterior to a reflector. ResultsA total of 100 stones were detected in 71 patients by non-contrast spiral CT. Of the 100 stones, 62 (62%) caused a posterior acoustic shadow and 58 (58%) showed echogenicity on B-mode ultrasound, while 68 (68%) caused the twinkle sign on color Doppler sonography. ConclusionIdentification of the twinkle sign on Doppler ultrasound can improve ultrasound detection of urinary calculi.

A Nonlocal Variational Model for Pansharpening Image Fusion

Pansharpening refers to the fusion process of inferring a high-resolution multispectral image from a high-resolution panchromatic image and a low-resolution multispectral one. In this paper we propose a new variational method for pansharpening which incorporates a nonlocal regularization term and two fidelity terms, one describing the relation between the panchromatic image and the high-resolution spectral channels and the other one preserving the colors from the low-resolution modality. The nonlocal term is based on the image self-similarity principle applied to the panchromatic image. The existence and uniqueness of minimizer for the described functional is proved in a suitable space of weighted integrable functions. Although quite successful in terms of relative error, state-of-the-art pansharpening methods introduce relevant color artifacts. These spectral distortions can be significantly reduced by involving the image self-similarity. Extensive comparisons with state-of-the-art algorithms are performed.

A sharp edge-preserving joint color demosaicking and zooming algorithm using integrated gradients and an iterative back-projection technique

Following the advances in single-sensor imaging techniques, interest in producing a zoomed full-color image from a Bayer mosaic data has been increased. Almost all of the recent approaches identified, with respect to the demosaicking step in the imaging pipeline, have chiefly focused on misguidance problems. However, in regions consisting of sharp edges or fine textures, these approaches are prone to large blurring effects. This paper proposes a new joint solution to overcome the above problems associated with demosaicking and zooming operations. On the basis of an enhanced soft-decision framework, we estimate the edge features by computing the integrated gradients. This allows the extraction of gradient information from both color intensity and color difference domains, simultaneously. Then, the edge guidance is incorporated in the interpolation of various stages to preserve edge consistency and improve computational efficiency. In addition, an edge-adaptive, iterative, back-projection technique is developed to compensate for image blurring as well as to further suppress color artifacts. Experimental results indicate that the new algorithm produces outstanding objective performances and sharp, visually pleasing color outputs, when compared to numerous other single-sensor image zooming solutions.

Field-Portable Pixel Super-Resolution Colour Microscope

Based on partially-coherent digital in-line holography, we report a field-portable microscope that can render lensfree colour images over a wide field-of-view of e.g., >20 mm2. This computational holographic microscope weighs less than 145 grams with dimensions smaller than 17×6×5 cm, making it especially suitable for field settings and point-of-care use. In this lensfree imaging design, we merged a colorization algorithm with a source shifting based multi-height pixel super-resolution technique to mitigate ‘rainbow’ like colour artefacts that are typical in holographic imaging. This image processing scheme is based on transforming the colour components of an RGB image into YUV colour space, which separates colour information from brightness component of an image. The resolution of our super-resolution colour microscope was characterized using a USAF test chart to confirm sub-micron spatial resolution, even for reconstructions that employ multi-height phase recovery to handle dense and connected objects. To further demonstrate the performance of this colour microscope Papanicolaou (Pap) smears were also successfully imaged. This field-portable and wide-field computational colour microscope could be useful for tele-medicine applications in resource poor settings.

영상의 국부적 특성을 고려한 색수차 보정 방법

In this paper, we propose a chromatic aberration removal algorithm in image capture devices, which considers local properties of the image. Chromatic aberration is generated by the fact that the refractive index of the lens is different for different wavelengths, which produces color artifacts on strong edge due to misalignment of RGB channels. Under the characteristics of the artifacts, the proposed algorithm first estimates the regions with the apparent color artifacts as the neighborhoods of the strong edge. In the regions, the proposed algorithm removes the color artifacts by matching the edges of RGB channels. The widely used conventional methods based on global image warping could not remove the color artifacts of longitudinal chromatic aberration and purple fringing identified by the image sensor, whereas the matching process of the proposed method could reduce them. Experimental results show that the proposed algorithm outperforms the conventional methods on objective and subjective criteria.

Wide-field computational color imaging using pixel super-resolved on-chip microscopy

Lens-free holographic on-chip imaging is an emerging approach that offers both wide field-of-view (FOV) and high spatial resolution in a cost-effective and compact design using source shifting based pixel super-resolution. However, color imaging has remained relatively immature for lens-free on-chip imaging, since a 'rainbow' like color artifact appears in reconstructed holographic images. To provide a solution for pixel super-resolved color imaging on a chip, here we introduce and compare the performances of two computational methods based on (1) YUV color space averaging, and (2) Dijkstra's shortest path, both of which eliminate color artifacts in reconstructed images, without compromising the spatial resolution or the wide FOV of lens-free on-chip microscopes. To demonstrate the potential of this lens-free color microscope we imaged stained Papanicolaou (Pap) smears over a wide FOV of ~14 mm(2) with sub-micron spatial resolution.

Color texture analysis using CFA chromatic co-occurrence matrices

Most color cameras are fitted with a single sensor that provides color filter array (CFA) images, in which each pixel is characterized by one of the three color components (either red, green, or blue). To produce a color image, the two missing color components have to be estimated at each pixel of the corresponding CFA image. This process is commonly referred to as demosaicing, and its result as the demosaiced color image.Since demosaicing methods intend to produce “perceptually satisfying” demosaiced color images, they attempt to avoid color artifacts. Because this is often achieved by filtering, demosaicing schemes tend to alter the local texture information that is, however, useful to discriminate texture images. To avoid this issue while exploiting color information for texture classification, it may be relevant to compute texture descriptors directly from CFA images.From chromatic co-occurrence matrices (CCMs) that capture the spatial interaction between color components, we derive new descriptors (CFA CCMs) for CFA texture images. Color textures are then compared by means of the similarity between their CFA CCMs. Experimental results achieved on benchmark color texture databases show the efficiency of this approach for texture classification.

An adaptive dynamic range compression with local contrast enhancement algorithm for real-time color image enhancement

Dynamic range compression has become an important function used in modern digital video cameras to improve visual quality of color images suffered from low dynamic range and poor contrast defects. This study addresses real-time implementation of an adaptive dynamic range compression algorithm for color image/video enhancement. To achieve this purpose, we first propose a new image-dependent nonlinear intensity-transfer function to produce a satisfactory dynamic range compression result with less color artifacts. The proposed algorithm is then derived by combining the new adaptive nonlinear intensity-transfer function with an efficient local contrast enhancement algorithm. Moreover, an algorithmic acceleration method is also presented to accelerate the processing speed of the proposed color image enhancement method, achieving real-time performance in processing high-definition video signals. Experimental results validate the performance of the developed method in terms of quantitative evaluation, visual quality, computational efficiency, and power consumption.

Correction of Axial and Lateral Chromatic Aberration With False Color Filtering

In this paper, we propose a chromatic aberration (CA) correction algorithm based on a false color filtering technique. In general, CA produces color distortions called color fringes near the contrasting edges of captured images, and these distortions cause false color artifacts. In the proposed method, a false color filtering technique is used to filter out the false color components from the chroma-signals of the input image. The filtering process is performed with the adaptive weights obtained from both the gradient and color differences, and the weights are designed to reduce the various types of color fringes regardless of the colors of the artifacts. Moreover, as preprocessors of the filtering process, a transient improvement (TI) technique is applied to enhance the slow transitions of the red and blue channels that are blurred by the CA. The TI process improves the filtering performance by narrowing the false color regions before the filtering process when severe color fringes (typically purple fringes) occur widely. Last, the CA-corrected chroma-signal is combined with the TI chroma-signal to avoid incorrect color adjustment. The experimental results show that the proposed method substantially reduces the CA artifacts and provides natural-looking replacement colors, while it avoids incorrect color adjustment.

Joint Demosaicing and Subpixel-Based Down-Sampling for Bayer Images: A Fast Frequency-Domain Analysis Approach

A portable device such as a digital camera with a single sensor and Bayer color filter array (CFA) requires demosaicing to reconstruct a full color image. To display a high resolution image on a low resolution LCD screen of the portable device, it must be down-sampled. The two steps, demosaicing and down-sampling, influence each other. On one hand, the color artifacts introduced in demosaicing may be magnified when followed by down-sampling; on the other hand, the detail removed in the down-sampling cannot be recovered in the demosaicing. Therefore, it is very important to consider simultaneous demosaicing and down-sampling.