This paper describes the method related to correcting color distortion in color imaging. Acquiring color images from
CMOS or CCD digital sensors can suffer from color distortion, which means that the image from sensors is different
from the original image in the color space. The main reasons are the cross-talks between adjacent pixels, the color
pigment characteristic's mismatch with human perception and infra-red (IR) influx to visible channel or red, green, blue
(RGB) due to IR cutoff filter imperfection. To correct this distortion, existing methods use multiplying gain coefficients
in each color channel and this multiplication can cause noise boost and loss of detail information. This paper proposes
the novel method which can not only preserve color distortion correction ability, but also suppress noise boost and loss
of detail information in the color correction process of IR corrupted pixels. In the case of non-IR corruption pixels, the
use of image before color correction instead of IR image makes this kind of method available. Specifically the color and
low frequency information in luminance channel is extracted from the color corrected image. And high frequency
information is from the IR image or the image before color correction. The method extracting the low and high
frequency information use multi-layer decomposition skill with edge preserving filters.
KEYWORDS: Image processing, Image quality, Digital cameras, Digital image processing, Cameras, Signal processing, RGB color model, Image fusion, Image sensors, Signal to noise ratio
Market's demands of digital cameras for higher sensitivity capability under low-light conditions are remarkably
increasing nowadays. The digital camera market is now a tough race for providing higher ISO capability. In this paper,
we explore an approach for increasing maximum ISO capability of digital cameras without changing any structure of an
image sensor or CFA. Our method is directly applied to the raw Bayer pattern CFA image to avoid non-linearity
characteristics and noise amplification which are usually deteriorated after ISP (Image Signal Processor) of digital
cameras. The proposed method fuses multiple short exposed images which are noisy, but less blurred. Our approach is
designed to avoid the ghost artifact caused by hand-shaking and object motion. In order to achieve a desired ISO image
quality, both low frequency chromatic noise and fine-grain noise that usually appear in high ISO images are removed
and then we modify the different layers which are created by a two-scale non-linear decomposition of an image. Once
our approach is performed on an input Bayer pattern CFA image, the resultant Bayer image is further processed by ISP
to obtain a fully processed RGB image. The performance of our proposed approach is evaluated by comparing SNR
(Signal to Noise Ratio), MTF50 (Modulation Transfer Function), color error ∝E*ab and visual quality with reference
images whose exposure times are properly extended into a variety of target sensitivity.
Dynamic range of natural scenes that we see in daily life ranges up to 120dB. Unfortunately, typical imaging devices
only cover about 50dB without any special circuit technique. To overcome this dynamic range problem, many algorithms
and devices have been developed and commercialized. However, commercialization in the field where image quality is
emphasized is not as active as in the filed where the image information is emphasized. This is because there are still
some limitations in capturing and displaying high dynamic range (HDR) image without loss of image information (color,
edge and contrast, etc.) In displaying HDR image, some losses of image information during tone reproduction are
inevitable, since the HDR image have to be processed with some kind of tone reproduction method to compress the
dynamic range fit to the low dynamic range (LDR) display devices. Also there is a report that the tone reproduced LDR
image on LDR display device is viewed better than HDR image on HDR display according to Oh [1]. For this reason, we
propose a new method which can tonally reproduce HDR image into a natural LDR image as auto exposed (AE) one
with minimum loss of image information.
KEYWORDS: Image enhancement, Image processing, RGB color model, Cameras, High dynamic range imaging, Image restoration, Digital imaging, Image sensors, Digital cameras, Linear filtering
In many cases, it is not possible to faithfully capture shadow and highlight image data of a high dynamic range (HDR)
scene using a common digital camera, due to its narrow dynamic range (DR). Conventional solutions tried to solve the
problem with an captured image which has saturated highlight and/or lack of shadow information. In this situation, we
introduce a color image enhancing method with the scene-adaptive exposure control. First, our method recommends an
optimal exposure to obtain more information in highlight by the histogram-based scene analysis. Next, the proposed
luminance and contrast enhancement is performed on the captured image. The main processing consists of luminance
enhancement, multi-band contrast stretching, and color compensation. The luminance and chrominance components of
input RGB data is separated by converting into HSV color space. The luminance is increased using an adaptive log
function. Multi-band contrast stretching functions are applied to each sub-band to enhance shadow and highlight at the
same time. To remove boundary discontinuities between sub-bands, the multi-level low-pass filtering is employed. The
blurred image data represents local illumination while the contrast-stretched details correspond to reflectance of the
scene. The restored luminance image is produced by the combination of multi-band contrast stretched image and multilevel
low-pass filtered image. Color compensation proportional to the amount of luminance enhancement is applied to
make an output image.
Digital images captured from CMOS image sensors suffer Gaussian noise and impulsive noise. To efficiently reduce the
noise in Image Signal Processor (ISP), we analyze noise feature for imaging pipeline of ISP where noise reduction
algorithm is performed. The Gaussian noise reduction and impulsive noise reduction method are proposed for proper
ISP implementation in Bayer domain. The proposed method takes advantage of the analyzed noise feature to calculate
noise reduction filter coefficients. Thus, noise is adaptively reduced according to the scene environment. Since noise is
amplified and characteristic of noise varies while the image sensor signal undergoes several image processing steps, it is
better to remove noise in earlier stage on imaging pipeline of ISP. Thus, noise reduction is carried out in Bayer domain
on imaging pipeline of ISP. The method is tested on imaging pipeline of ISP and images captured from Samsung 2M
CMOS image sensor test module. The experimental results show that the proposed method removes noise while
effectively preserves edges.
A series of psychophysical experiments using paired comparison method was performed to investigate various visual
attribute affecting image quality of a mobile display. An image quality difference model was developed to show high
correlation with visual results. The result showed that Naturalness and Clearness are the most significant attributes
among the perceptions. A colour quality difference model based on image statistics was also constructed and it was
found colour difference and colour naturalness are important attributes for predicting image colour quality difference.
The purpose of this study is to examine the difference in perceptual brightness enhancement per image category through perceptual brightness measurement. Perceptual brightness is measured via psychophysical experiment and brightness enhancement is performed by TMF (Tone Mapping Function). The classification process is comprised of two steps. It is possible to classify histograms into six groups. The three different TMFs for each category selected using the criteria and TMF application strengths. A psychophysical experiment to measure perceptual brightness enhancement was carried out. The experiment was to determine the equal perceptual brightness point between an original image and the corresponding set of TMF images. The results showed that the mean luminance for each category is significantly different. The results from brightness matching indicate that perceptual brightness enhancement is dependent on image category. We can propose that image category should be considered for advanced brightness enhancement methods.
KEYWORDS: Error analysis, Flat panel displays, Human vision and color perception, Plasma display panels, LCDs, RGB color model, Plasma, Pixel resolution, Image enhancement, Visual system
This study investigates the color error problems posed by large flat panel displays and proposes a subpixel-rendering algorithm to mitigate the problem. The color error problems are caused by Mach band effect and the convergence error of a pixel on large subpixel structured displays and named a color band error. The proposed method includes three processes; a finding process of areas or pixels generating the error, an estimating process of the error, and a correction process of the error. To correct the color band error, we take an error erosion approach, an error concealment approach, and a hybrid approach of the error erosion and the error concealment. In this paper, we experimented to know the threshold where human vision can detect by a psychophysical method. In addition, we applied our proposed method to a commercial 42" plasma display to confirm the effect. The results show that all observers see the color band error at a sharp edge having above 64-gray difference and the converted test images by our algorithm are preferred to the original test images. Finally, this paper reports that the Mach band effect and the convergence error on large subpixel structured display produce color band errors on images having sharp edge and the proposed method effectively corrects the color band errors.
KEYWORDS: System on a chip, RGB color model, LCDs, Optimization (mathematics), Visualization, CRTs, Image quality, Image quality standards, Color imaging, Video
The theoretical approach is introduced to design the optimal chromaticities for primaries of a display with a given size of triangular color gamut in xy-plane. Optimal primaries are defined as a set of chromaticities of red, green and blue primaries with fixed white point that most optimally satisfying four criteria, i.e. gamut size, gamut shape, coverage of object colors and hue of the primaries, in the visually uniform color space, CIECAM02. It is assumed that the optimal gamut should cover that of sRGB and have similar maximum chroma for each hue. The number of SOCS data located outside the gamut is used as a criterion to judge the coverage of object colors. Also it is set the hues of primaries to be close to those of sRGB. The simulation results showed that the optimal primaries for 85% of NTSC area have similar points with sRGB for red and blue, and green primary is located in between sRGB and NTSC. For 100% of NTSC area, the optimal chromaticities are located near those of NTSC for red and green and that of sRGB for blue.
The recent color display market is being focused on displays with larger panel size and larger color gamut. More specifically, for widening the color gamut, multi-primary display (MPD), which is a display having more than the conventional three-channels, has been an important issue. However, developing MPD faces many difficulties such as sustaining display luminance against 3-color displays, embodying a simple H/W structure, and assigning new color signals based on input RGB signals.
The purpose of this study is to propose a method to display color on a new pixel structure for flat-panel displays with six primary colors intended to expand the color gamut, and to discuss a color decomposition algorithm for this new structure to assign signals to 6-channel from an input RGB signal. Special interests of this study are made on minimizing the deterioration of color image quality compared with RGB-based display and maximizing the usage of the widened color gamut. The result of this study was implemented on a prototype 6-color LCD and verified generation of the color signals for 6-color without luminance degradation. The device is capable of reproducing colors like emerald cyan, which cannot be displayed on a conventional RGB display. In addition, hardware implementation on FPGA verified commercial viability of the algorithm.
This paper proposes a color decomposition method for a multi-primary display (MPD) using a 3-dimensional look-up-table (3D-LUT) in linearized LAB space. The proposed method decomposes the conventional three primary colors into multi-primary control values for a display device under the constraints of tristimulus matching. To reproduce images on an MPD, the color signals are estimated from a device-independent color space, such as CIEXYZ and CIELAB. In this paper, linearized LAB space is used due to its linearity and additivity in color conversion. First, the proposed method constructs a 3-D LUT containing gamut boundary information to calculate the color signals for the MPD in linearized LAB space. For the image reproduction, standard RGB or CIEXYZ is transformed to linearized LAB, then the hue and chroma are computed with reference to the 3D-LUT. In linearized LAB space, the color signals for a gamut boundary point are calculated to have the same lightness and hue as the input point. Also, the color signals for a point on the gray axis are calculated to have the same lightness as the input point. Based on the gamut boundary points and input point, the color signals for the input point are then obtained using the chroma ratio divided by the chroma of the gamut boundary point. In particular, for a change of hue, the neighboring boundary points are also employed. As a result, the proposed method guarantees color signal continuity and computational efficiency, and requires less memory.
The term "color temperature" represents the color of light source or the white point of image displaying devices such as TV and PC monitor. By controlling the color temperature, we can convert the reference white color of images. This is equivalent to the illuminant change, which alters all colors in the scene. In this paper, our goal is to find an appropriate method of converting the color temperature in order to reproduce the user-preferred color temperature in video displaying devices. It is essential that the relative difference of color temperature between successive image frames should be well preserved as well as the appearance of images should seem natural after applying the user-preferred color temperature. In order to satisfy these conditions, we propose an adaptive color temperature conversion method that estimates the color temperature of an input image and determines the output color temperature in accordance with the value of the estimated one.
This paper proposes a uniform color sample selection and color halftoning method based on color correction using neural network with a set of uniform color samples and selective vector error diffusion for enhancing color reproduction on a printer. In order to generate uniform color samples in CIELAB color space, a set of uniformly populated color samples in a CIELAB printer gamut and monitor gamut are calculated by LBG (Linde, Buzo, Gray) quantization algorithm. Then, the corresponding device- dependent values of CMY and RGB are estimated by a trained NN, which was temporally trained by a set of uniform samples in the device-dependent spaces.
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