Ho-Gun Ha

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.

Noise Reduction Based on Partial-Reference, Dual-Tree Complex Wavelet Transform Shrinkage

This paper presents a novel way to reduce noise introduced or exacerbated by image enhancement methods, in particular algorithms based on the random spray sampling technique, but not only. According to the nature of sprays, output images of spray-based methods tend to exhibit noise with unknown statistical distribution. To avoid inappropriate assumptions on the statistical characteristics of noise, a different one is made. In fact, the non-enhanced image is considered to be either free of noise or affected by non-perceivable levels of noise. Taking advantage of the higher sensitivity of the human visual system to changes in brightness, the analysis can be limited to the luma channel of both the non-enhanced and enhanced image. Also, given the importance of directional content in human vision, the analysis is performed through the dual-tree complex wavelet transform (DTWCT). Unlike the discrete wavelet transform, the DTWCT allows for distinction of data directionality in the transform space. For each level of the transform, the standard deviation of the non-enhanced image coefficients is computed across the six orientations of the DTWCT, then it is normalized. The result is a map of the directional structures present in the non-enhanced image. Said map is then used to shrink the coefficients of the enhanced image. The shrunk coefficients and the coefficients from the non-enhanced image are then mixed according to data directionality. Finally, a noise-reduced version of the enhanced image is computed via the inverse transforms. A thorough numerical analysis of the results has been performed in order to confirm the validity of the proposed approach.

Estimation of reflectance based on properties of selective spectrum with adaptive Wiener estimation

To accurately represent the colors in a real scene, a multi-channel camera system is necessary. One of the applications of the data acquired with the multi-channel camera system is the spectral reflectance estimation. One of the most widely used methods to estimate the spectral reflectance is the Wiener estimation. While simple and accurate in controlled conditions, the Wiener estimation does not perform as well with real scene data. Therefore, the adaptive Wiener estimation has been proposed to improve the performance of the Wiener estimation. The adaptive Wiener estimation uses a similar training set that was adaptively constructed from the standard training set according to the camera responses. In this paper, a new way of constructing such similar training set using the correlation between each spectral reflectance in the standard training set and the first approximation of the spectral reflectance that was obtained by the Wiener estimation is proposed. The experimental results showed that the proposed method is more accurate than the conventional Wiener estimations.

Subpixel shift estimation in noisy image using a wiener filtered local region

Image registration is a fundamental technique in image processing that requires a precise shift estimation with subpixel accuracy. However, when an image is affected by noise, aliasing, or other artifacts, this increases the estimation error. Accordingly, a novel accurate subpixel shift estimation method is proposed for the registration of noisy images. In the proposed method, instead of using the whole noisy image, a local region is selected based on analyzing the gradient and distribution of the frequency component in the vicinity of a corner. Next, an adaptive Wiener filter, where the window size is varied depending on the property of the local region, is applied to suppress remaining noise. Finally, an iterative phase correlation of the selected local region is performed within a restricted range using a coarse to fine method. In experiments, the proposed method provides a higher estimation accuracy than other methods. Plus, when applying the proposed method to the registration of super resolution, an enhanced edge sharpness is seen in the resulting images.

Color enhancement for faded images based on multi-scale gray world algorithm

This paper presents a color enhancement algorithm based on multi-scale gray world algorithm for faded images. First, the proposed method adopts local process by using multi-scale mask. The coefficients for each multi-scale mask are obtained to apply the gray world algorithm. Then, integrating the coefficients with weights is performed to calculate correction ratio for red and blue channels in the gray world assumption. Finally, the corrected image is obtained by applying the integrated coefficients to the gray world algorithm. In the experimental results, the proposed method reproduces better colors for both wholly and partially faded images compared with the previous methods.

An automatic color correction method inspired by the retinex and opponent colors theories

Color correction indicates the process of changing the colors of a digital image in order to reach given objectives. Usually the criteria under which color correction is performed match those of white-balancing and color constancy. In this work we present and automatic color correction method inspired by the Retinex and opponent colors theories with theoretical applicability on images with multiple illuminants. The method here presented has been tested in the field of skin tone color correction.

Selective skin tone reproduction using preferred skin colors

People in a color image, particularly facial patterns are important and interesting visual object. Thus, color reproduction for skin tone becomes the foremost treatment, because skin color is a key memory color in color application systems. Previous studies suggested skin color reproduction by mapping only to the center value of preferred skin region. However, it is not suitable to determine one preference color because preference color from the observers preference test is not dominant. In this paper, selective skin color reproduction using preferred skin colors is proposed. The proposed method first defines preferred skin colors according to their luminance level. Next skin color is detected by using skin and feature map. After that, each corresponding preferred skin color is determined for each skin region. Finally, input skin color is proportionally mapped toward preferred skin color according to the difference between input skin color and preferred skin color, smoothly reproducing skin color. Through the experimental results, the proposed method achieves preferred skin color reproduction.

Simultaneous color matching in stereoscopic images based on image decomposition

Color discrepancies between the left and right images from a stereoscopic camera cause many problems, including a reduction in the three-dimensional effect and increased visual fatigue. Thus, color matching of stereoscopic images is very important for three-dimensional display systems. Therefore, this paper proposes simultaneous color matching based on image decomposition. The stereoscopic images are first decomposed into various levels, and the colors are then matched using cumulative histogram matching and a Retinex algorithm. Experimental results show that the color matching performance of the proposed method is better than that of previous methods.

Accurate Wiener Estimation by Constructing a Similar Training Set Based on Spectral Correlation

An image is generally acquired from traditional digital camera with three channels, such as red, green, and blue. However, an RGB image cannot fully represent real scene. To accurately represent the colors in a real scene, a multi-channel camera system is used to estimate spectral reflectance. Wiener estimation is widely used methods for estimating the spectral reflectance. While simple and accurate in controlled conditions, the Wiener estimation does not perform as well with real scene data. Therefore, the adaptive Wiener estimation has been proposed to improve the performance of the Wiener estimation. It uses a similar training set that was adaptively constructed from the standard training set. In this paper, a new way of constructing similar training set is proposed by using the correlation between spectral reflectance in the standard training set and the approximated spectral reflectance by the Wiener estimation. The experimental results show that the proposed method is more accurate than the conventional Wiener estimations.

Hue-shift model for DLP projector with the white peaking function

Projectors have become common display devices, not only for office and school presentations, but also for home theater entertainment. Although a completely dark room is the ideal venue for watching a projected image, in most situations (including classrooms and conference rooms) the viewing conditions are not completely dark, and ambient light falling on the screen produces a background light level with the image projected on top. As the background light increases, it becomes more difficult to see the projected image, which becomes dull and may appear washed out. What is really happening is that the ambient light reduces the contrast of the image. While the amount of light contributing to the image remains the same, more light has been projected onto the screen by other light sources. This effect can be reduced by employing the white-peaking function of a digital light-processing (DLP) projector, which adjusts the white segment of the color wheel, resulting in more natural and vivid images. Although the chromaticity coordinates for an image projected with and without white peaking are the same, when white is added to the projected image, the perceived hue changes. This phenomenon is known as the Abney effect. This paper presents a model of this hue-shift phenomenon and proposes a hue-correction method. For evaluation purposes, an observer-preference test is conducted on several test images with and without hue shifts, and z-scores are utilized to compare the results.