Tae-Hyoung Lee

Adaptive color enhancement based on multi-scaled Retinex using local contrast of the input image

Methods of contrast enhancement and color correction are currently among the most active fields in imaging research. The tone curve or histogram of an image is generally used to improve contrast and detail, even though this is often unsatisfactory because the intensity and chromaticity of the illumination vary with geometric position. This has led to the development of the multi-scaled Retinex algorithm in which the in fluence of non-uniform illumination is reduced by partitioning the original image using local average images that are estimated ba sed on Gaussian filtering. However, this algorithm produces color distortion because of the dominant chromaticity of the illumination. To solve this problem, this study introduces a new color correction method for digital images inspired by the multi-scale Retinex algorithm. The method includes selecting appropriate parameters for the Gaussian filters because this is the most important factor in reducing the artifacts introduced by the conventional multi-scale Retinex algorithm. Measures of visual contrast and halo are used to check the condition of artifacts for different values of the Gaussian parameters. The illuminant component is estimated to correct its chromaticity. A saturation compensation method based on preserving the chroma ratio is also used to compensate for the possible lack of saturation resulting from the modified multi-scale Retinex model.

Improved color reproduction based on CIELAB color space in integrated multi-scale retinex

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.

Photo-inkjet printing method based on limited colorant amount and dot-visibility ordering

This paper proposes a method for reducing unnecessary colorant usage based on limiting the total-colorant quantity and dot-visibility ordering. First, CIELAB values are estimated for an input RGB image using a color-mixing model, and then compared with pre-calculated CIELAB values corresponding to all combinations of the CMYKlclm colorants using a color-difference constraint, thereby determining the initial CMYKlclm candidates. Next, a limitation on the total-colorant quantity is imposed on the initial CMYKlclm candidates to prevent a poor edgesharpness, and the final CMYKlclm candidates are then determined based on different dot-visibility orderings of C, M, Y, K, lc, and lm to reduce the use of excessive amounts of colorants. Experiments show that the proposed method can effectively reduce excessive colorant usage, while preserving a good image quality.

Efficient HDR image acquisition using estimation of scenic dynamic range in camera images with different exposures

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.

Hue-Shift Modeling and Correction Method for High-Luminance Display

The human eye usually experiences a loss of color sensitivity when it is subjected to high levels of luminance, and perceives a discrepancy in color between high and normal-luminance displays, generally known as a hue shift. Accordingly, this paper models the hue-shift phenomenon and proposes a hue-correction method to provide perceptual matching between high and normal-luminance displays. To quantify the hue-shift phenomenon for the whole hue angle, 24 color patches with the same lightness are first created and equally spaced inside the hue angle. These patches are then displayed one-by-one on both displays with different luminance levels. Next, the hue value for each patch appearing on the high-luminance display is adjusted by observers until the perceived hue for the patches on both displays appear the same visually. After obtaining the hue-shift values from the color matching experiment, these values are fit piecewisely into seven sinusoidal functions to allow hue-shift amounts to be approximately determined for arbitrary hue values of pixels in a high-luminance display and then used for correction. Essentially, an input RGB image is converted to CIELAB LCh (lightness, chroma, and hue) color space to obtain the hue values for all the pixels, then these hue values are shifted according to the amount calculated by the functions of the hue-shift model. Finally, the corrected image is inversely converted to an output RGB image. For evaluation, a matching experiment was performed using several test images and z-score comparisons.

Modeling for hue shift effect of human visual system on high luminance display

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.

Estimation of low dynamic range images from single Bayer image using exposure look-up table for high dynamic range image

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. Observers preference test shows that HDR images from the proposed method provides similar appearance with the result images using captured images.

Characteristic of color gamut related with MPEG2 compression

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.