Hyun Mook Oh

Region adaptive color demosaicing algorithm using color constancy

This paper proposes a novel way of combining color demosaicing and the auto white balance (AWB) method, which are important parts of image processing. Performance of the AWB is generally affected by demosaicing results because most AWB algorithms are performed posterior to color demosaicing. In this paper, in order to increase the performance and efficiency of the AWB algorithm, the color constancy problem is examined during the color demosaicing step. Initial estimates of the directional luminance and chrominance values are defined for estimating edge direction and calculating the AWB gain. In order to prevent color failure in conventional edge-based AWB methods, we propose a modified edge-based AWB method that used a predefined achromatic region. The estimation of edge direction is performed region adaptively by using the local statistics of the initial estimates of the luminance and chrominance information. Simulated and real Bayer color filter array (CFA) data are used to evaluate the performance of the proposed method. When compared to conventional methods, the proposed method shows significant improvements in terms of visual and numerical criteria.

Edge adaptive color demosaicking based on the spatial correlation of the bayer color difference

An edge adaptive color demosaicking algorithm that classifies the region types and estimates the edge direction on the Bayer color filter array (CFA) samples is proposed. In the proposed method, the optimal edge direction is estimated based on the spatial correlation on the Bayer color difference plane, which adopts the local directional correlation of an edge region of the Bayer CFA samples. To improve the image quality with the consistent edge direction, we classify the region of an image into three different types, such as edge, edge pattern, and flat regions. Based on the region types, the proposed method estimates the edge direction adaptive to the regions. As a result, the proposed method reconstructs clear edges with reduced visual distortions in the edge and the edge pattern regions. Experimental results show that the proposed method outperforms conventional edge-directed methods on objective and subjective criteria.

Color Restoration for Infrared Cutoff Filter Removed RGBN Multispectral Filter Array Image Sensor

Imaging systems based on multispectral filter arrays(MSFA) can simultaneously acquire wide spectral information. A MSFA image sensor with R, G, B, and near-infrared(NIR) filters can obtain the mixed spectral information of visible bands and that of the NIR bands. Since the color filter materials used in MSFA sensors were almost transparent in the NIR range, the observed colors of multispectral images were degraded by the additional NIR spectral band information. To overcome this color degradation, a new signal processing approach is needed to separate the spectral information of visible bands from the mixed spectral information. In this paper, a color restoration method for imaging systems based on MSFA sensors is proposed. The proposed method restores the received image by removing NIR band spectral information from the mixed wide spectral information. To remove additional spectral information of the NIR band, spectral estimation and spectral decomposition were performed based on the spectral characteristics of the MSFA sensor. The experimental results show that the proposed method restored color information by removing unwanted NIR contributions to the RGB color channels.

Color rolling suppression algorithm considering luminance and color constancy

When a digital image sequence is sampled with a periodically emitting light source, the color rolling (CR) phenomenon occurs, which is shown by periodical variations of color and luminance values. In conventional CR suppression (CRS) methods, color variation has been reduced by using auto white balance methods. However, the CR phenomenon still appears in the resulting image sequences due to interfield illuminant intensity variation. In the proposed CRS method, the interfield luminance and color variations are simultaneously suppressed by estimating the illuminant change between the current and the target fields. In order to consider the object motions, a motion detection technique is used to estimate the luminance changes that occurred due to the CR phenomenon. Moreover, the illuminant color is estimated using the CR achromatic color distribution in the chromaticity space which is founded on the periodicity of the CR phenomenon. Based on the motion detection and the achromatic color detection techniques, the illuminant is estimated using the obtained color components in a common area of both static and achromatic regions. The experimental results demonstrate that our strategy efficiently suppresses the CR phenomenon without being affected by moving objects and produces luminance and color constant image sequences.

Region adaptive color demosaicing using cross-channel correlation

In this paper, we consider the problem of recovering full-color images from color-sampled observation. Each pixel location is classified into flat, edge and pattern region and edge indicator function is used to avoid artifacts in high frequency regions and improve the performance. The horizontal and vertical direction of edge is decided using local statistics based on the concepts of spectral-spatial correlation. As a postprocessing, the modified filtering on color difference domain is adopted to improve the quality of the image. Experimental results illustrate the benefits of the proposed method. When compared to the conventional methods, the proposed method outperforms them on quantitative and qualitative criteria.