KEYWORDS: High dynamic range imaging, Image processing, Cameras, Image analysis, RGB color model, Protactinium, Digital imaging, Digital cameras, Control systems, Light
High dynamic range(HDR) imaging is a technique to represent the wider range of luminance from the lightest and
darkest area of an image than normal digital imaging techniques. These techniques merge multiple images, called as
LDR(low dynamic range) or SDR(standard dynamic range) images which have proper luminance with different exposure
steps, to cover the entire dynamic range of real scenes. In the initial techniques, a series of acquisition process for LDR
images according to exposure steps are required. However, several acquisition process of LDR images induce ghost
artifact for HDR images due to moving objects. Recent researches have tried to reduce the number of LDR images with
optimal exposure steps to eliminate the ghost artifacts. Nevertheless, they still require more than three times of
acquisition processes, resulting ghosting artifacts. In this paper, we propose an HDR imaging from a single Bayer image
with arbitrary exposures without additional acquisition processes. This method first generates new LDR images which
are corresponding to each average luminance from user choices, based on Exposure LUTs(look-up tables). Since the
LUTs contains relationship between uniform-gray patches and their average luminances according to whole exposure
steps in a camera, new exposure steps for any average luminance can be easily estimated by applying average luminance
of camera-output image and corresponding exposure step to LUTs. Then, objective LDR images are generated with new
exposure steps from the current input image. Additionally, we compensate the color generation of saturated area by
considering different sensitivity of each RGB channel from neighbor pixels in the Bayer image. Resulting HDR images
are then merged by general method using captured images and estimated images for comparison. Observer's preference
test shows that HDR images from the proposed method provides similar appearance with the result images using
captured images.
KEYWORDS: Cameras, RGB color model, Projection systems, Roentgenium, Digital cameras, Color reproduction, Electro optical modeling, Instrument modeling, Distortion, Digital imaging
Recently, projector is one of the most common display devices not only for presentation at offices and classes, but for
entertainment at home and theater. The use of mobile projector expands applications to meeting at fields and presentation
on any spots. Accordingly, the projection is not always guaranteed on white screen, causing some color distortion.
Several algorithms have been suggested to correct the projected color on the light colored screen. These have limitation
on the use of measurement equipment which can't bring always, also lack of accuracy due to transform matrix obtained
by using small number of patches. In this paper, color correction method using general still camera as convenient
measurement equipment is proposed to match the colors between on white and colored screens. A patch containing 9
ramps of each channel are firstly projected on white and light colored screens, then captured by the camera, respectively,
Next, digital values are obtained by the captured image for each ramp patch on both screens, resulting in different values
to the same patch. After that, we check which ramp patch on colored screen has the same digital value on white screen,
repeating this procedure for all ramp patches. The difference between corresponding ramp patches reveals the quantity of
color shift. Then, color correction matrix is obtained by regression method using matched values. Differently from
previous methods, the use of general still camera allows to measure regardless of places. In addition, two captured
images on white and colored screen with ramp patches inform the color shift for 9 steps of each channel, enabling
accurate construction of transform matrix. Nonlinearity of camera characteristics is also considered by using regression
method to construct transform matrix. In the experimental results, the proposed method gives better color correction on
the objective and subjective evaluation than the previous methods.
Image compression techniques such as JPEG and MPEG induce losses of image quality. Representative specifications
are blocking artifact, color bleeding, and smearing. These losses are usually investigated on the spatial distortions from
reconstructed images such as MSE(mean square error) and PSNR(peak signal to noise ratio). However, color
information is practically influenced by compression techniques. The distortion of color information is shown as
distorted information of gamut characteristics such as gamut size in the reconstructed images. Accordingly, this paper
introduces the investigation of the relationship between image compression and the gamut characteristics for MPEG-2
compression. Some image quality metrics are introduced; gamut size and gamut fidelity using unique color and CDI
(color distribution index), respectively. The influence of moving object is first observed with time sequential. Then,
deterioration due to the variation of bit rate is observed using gamut size and gamut characteristics. Results shows the
moving objects do not influence a lot to the gamut characteristic, however, the decrease of bit rate gives lots of
deterioration for gamut characteristics shown as the variation of CDI.
KEYWORDS: Image enhancement, RGB color model, Distortion, Visibility, Image processing, Color reproduction, Digital imaging, High dynamic range imaging, Visual system, Associative arrays
Recently, tone reproduction is widely used in the field of image enhancement and HDR imaging. This method is
especially used to provide the proper luminance so that captured images give the same sensation as the scene. As a result,
we can get high contrast and naturalness of colors. There is ample literature on the topic of tone reproduction that has the
objective of reproducing natural looking color in digital images. In recent papers, IMSR (Integrated multi-scale Retinex)
shows great naturalness in the result images. Most methods, including IMSR, work in RGB or quasi-RGB color spaces,
although some method adopted the use of luminance. This raises hue distortion from the point of the human visual
system, that is, hue distortion in CIELAB color space. Accordingly, this paper proposes an enhanced IMSR method in a
device-independent color space, CIELAB, to preserve hue and obtain high contrast and naturalness. In order to achieve
the devised objectives, a captured sRGB image is transformed to the CIELAB color space. IMSR is then applied to only
L* values, thus the balance of colors components are preserved. This process causes unnatural saturation, therefore
saturation adjustment is performed by applying the ratio of chroma variation at the sRGB gamut boundary according to
the corrected luminance. Finally, the adjusted CIELAB values are transformed to sRGB using the inverse transform
function. In the result images of the proposed method, containing both high and low luminance regions, visibility in dark
shadow and bright regions was improved and color distortion was reduced.
KEYWORDS: High dynamic range imaging, Cameras, Image acquisition, Digital imaging, Digital cameras, Image analysis, Iris, Image storage, RGB color model, Error analysis
Generally, to acquire an HDR image, many images that cover the entire dynamic range of the scene with different exposure times are required, then these images are fused into one HDR image. This paper proposes an efficient method for the HDR image acquisition with small number of images. First, we estimated scenic dynamic range using two images with different exposure times. These two images contain the upper and lower limit of the scenic dynamic range. Independently of the scene, according to varied exposure times, similar characteristics for both the maximum gray levels in images that include the upper limit and the minimum gray levels in images that include the lower limit are identified. After modeling these characteristics, the scenic dynamic range is estimated using the modeling results. This estimated scenic dynamic range is then used to select the proper exposure times for the acquisition of an HDR image. We selected only three proper exposure times because entire dynamic range of the cameras could be covered by three dynamic range of the cameras with different exposure times. To evaluate the error of the HDR image, experiments using virtual digital camera images were carried out. For several test images, the error of the HDR image using proposed method was comparable to that of the HDR image which utilize more than ten images for the HDR image acquisition.
This paper proposes a color correction method based on modeling the hue shift phenomenon of human visual system
(HVS). Observers tend to perceive same color stimuli, but of different intensity, as different hues, what is referred to as
the hue shift effect. Although the effect can be explained with the Bezold-Brücke (B-B) effect, it is not enough to apply
the B-B model on high luminance displays because most displays have a broad-band spectrum distribution and results
vary according to type of display. In the proposed method, the quantities of hue shift between a high luminance display
and a normal luminance display were first modeled by a color matching experiment with color samples along the hue
angle of the LCH color space. Based on the results, the hue shift was then modeled piecewise and was finally applied to
the inverse characterization of display to compensate the original input image. From evaluating the proposed method
using the psychophysical experiment with some test images, we confirmed that the proposed modeling method is
effective for color correction on high luminance displays.
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