Dae-Chul Kim

Real-time multi-scale Retinex to enhance night scene of vehicular camera

Recently, image processing has been commonly applied to various parts of the vehicle industry. In particular, the use of vehicle cameras gives the driver clear view for occluded regions to the front or rear of the vehicle. However, images from the camera have low visibility at night due to the lack of ambient illumination. To enhance the visibility of images, generic methods using global functions, such as the gamma function, can be used for real time processing, but these methods cannot improve local visibility. In this paper, we propose real time processing of night scene images based on modified multi-scale Retinex(MSR) to enhance the visibility of images from vehicle camera. This method uses integer operations and bit-sifting for real-time computation. In addition, we apply simple blur and mean filters instead of Gaussian convolution to reduce computational cost. Color balance is preserved by obtaining the surround image for the luminance channel only. In the experiments, processing speed approximates to 30 frame per second(FPS) and dark regions in the frames are improved through enhancement of local visibility.

Visibility Enhancement of Mobile Device Through Luminance and Chrominance Compensation Upon Hue

The visibility of images displayed on mobile devices can significantly vary according to the lighting conditions. Generally, increasing the lighting scale produces a more compressed perceived color gamut. In addition, the compression rate of the perceived color gamut varies according to the hue angle. Therefore, this paper presents a method of compensating the luminance and chrominance according to the hue of the image displayed on a mobile device. Two psychophysics experiments are initially performed to analyze the human perception of luminance and chrominance. The perceived luminance and perceived chrominance reduction rates are then calculated for the primary colors. Next, to calculate the perceived luminance and chrominance reduction rates for all hue angles, piecewise linear interpolation is used between each primary color hue angle. Finally, the luminance and chrominance compensation rates are calculated by using the precalculated perceived luminance and chrominance reduction rates. The luminance and chrominance compensation rates according to the hue angle are then applied to an input image. Experimental results show that the proposed method enhances the colors in indoor and outdoor environments.

Multi-spectral flash imaging using weight map

In order to acquire images in low-light environments, it is usually necessary to adopt long exposure times or resort to flash lights. However, flahses often induce color distortion, cause the red-eye effect and can be disturbing to subjects. On the other hand, long-exposure shots are susceptible to subject-motion, as well as motion-blur due to camera shake when performed hand-held. A recently introduced technique to overcome the limitations of traditional low-light photography is that of multi-spectral flash. Multi-spectral flash images are a combination of UV/IR and visible spectrum information. The general idea is that of retrieving details from the UV/IR spectrum and color from the visible spectrum. However, multi-spectral flash images themselves are subject to color distortion and noise. This works presents a method to compute multi-spectral flash images so that noise can be reduced and color accuracy improved. The proposed approach is a previously seen optimization method, improved by the introduction of a weight map used to discriminate uniform regions from detail regions. The optimization target function takes into account the output likelihood with respect to the ambient light image, the sparsity of image gradients as well as the spectral constraints for the IR-red channels and UV-blue channels. The performance of the proposed method has been objectively evaluated using long-exposure shots as references.

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.

Image thresholding using standard deviation

Threshold selection using the within-class variance in Otsu’s method is generally moderate, yet inappropriate for expressing class statistical distributions. Otsu uses a variance to represent the dispersion of each class based on the distance square from the mean to any data. However, since the optimal threshold is biased toward the larger variance among two class variances, variances cannot be used to denote the real class statistical distributions. Therefore, to express more accurate class statistical distributions, this paper proposes the within-class standard deviation as a criterion for threshold selection, and the optimal threshold is then determined by minimizing the within-class standard deviation. Experimental results confirm that the proposed method produced a better performance than existing algorithms.

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.

Habit modification of tamoxifen crystals using antisolvent crystallizations

The crystal habit of tamoxifen was modified using antisolvent crystallization techniques. Tamoxifen was crystallized from organic solvents using two different antisolvents (water and carbon dioxide). The habit of the precipitated crystals was modified by changing the process conditions, such as the solution and antisolvent mixing rate, the organic solvent, the presence of ultrasonic waves, and the addition of external additives. Particle size, crystal habit, particle aspect ratio and powder XRD patterns of the precipitated tamoxifen crystals were measured. With water as the antisolvent, the particle size of tamoxifen was significantly reduced compared to that of the raw material. When the antisolvent was carbon dioxide, the particle size was an order of magnitude greater than that of the raw material. The average aspect ratio of the tamoxifen crystals ranged from 1.8 to 16.2. The presence of ultrasonic waves caused a significant reduction in the aspect ratio, as well as the particle size. Furthermore, the addition of external additives was found to influence the crystal habit of tamoxifen.

Illumination estimation using nonnegative matrix factorization and dominant chromaticity analysis

Human vision can perceive object colors as being the same as colors under a white illuminant. However, images captured by a camera are influenced by the chromaticity of the illumination. Therefore, various illumination estimation algorithms have already been proposed for removing the chromaticity of illuminations in an image to improve the image quality. Most recently, NMFsc (nonnegative matrix factorization with sparseness constraint) was introduced to extract the illumination and reflectance component using nonnegative matrix decomposition and sparseness constraints. However, if an image has a dominant object, the sparse constraint values include the dominant chromaticity of that object, thereby inducing color distortion. Therefore, this paper suggests illuminant estimation using a combination of the conventional NMFsc and a dominant chromaticity analysis. First, the dominant chromaticity region in an image is selected using a chromaticity histogram and the standard deviation. The non-negative matrix decomposition and sparseness constraints are then separately applied to the dominant chromaticity region and non-dominant chromaticity region. Finally, the illumination in an image is estimated by combining the low sparse constraint values that exclude the dominant chromaticity. The performance of the proposed method is evaluated using the angular error for the Ciurea 11,346 image data set, and the experimental results confirm that the proposed method reduces the angular error more than previous methods.

Moire reduction using inflection point in frequency domain

Digital still cameras generally use an optical low-pass filter(OLPF) to enhance the image quality by removing high spatial frequencies causing aliasing. While eliminating the OLPF can save manufacturing costs, images captured without using an OLPF include moiré in the high spatial frequency region of the image. Therefore, to reduce the presence of moiré in a captured image, this paper presents a moiré reduction method without the use of an OLPF. First, the spatial frequency response(SFR) of the camera is analyzed and moiré regions detected using patterns related to the SFR of the camera. Using these detected regions, the moiré components represented by the inflection point between the high frequency and DC components in the frequency domain are selected and then removed. Experimental results confirm that the proposed method can achieve moiré reduction while preserving detail information.

Enhancement of Visibility Using App Image Categorization in Mobile Device

Mobile devices are generally using app images which are artificially designed. Accordingly, this paper presents adjusting device brightness based on app image categorization for enhancing the visibility under various light condition. First, the proposed method performed two prior subjective tests under various lighting conditions for selecting features of app images concerning visibility and for selecting satisfactory range of device brightness for each app image. Then, the relationship between selected features of app image and satisfactory range of device brightness is analyzed. Next, app images are categorized by using two features of average brightness of app image and distribution ratio of advanced colors that are related to satisfaction range of device brightness. Then, optimal device brightness for each category is selected by having the maximum frequency of satisfaction device brightness. Experimental results show that the categorized app images with optimal device brightness have high satisfaction ratio under various light conditions.