• Journal of the Institute of Electronics and Information Engineers
• /
• v.52 no.8
• /
• pp.89-96
• /
• 2015

Human visual system has chromatic adaptation to determine the color of an object regardless of illumination, whereas digital camera records illumination and reflectance together, giving the color appearance of the scene varied under different illumination. NMFsc(nonnegative matrix factorization with sparseness constraint) was recently introduced to estimate original object color by using sparseness constraint. In NMFsc, low sparseness constraint is used to estimate illumination and high sparseness constraint is used to estimate reflectance. However, NMFsc has an illumination estimation error for images with large uniform area, which is considered as dominant chromaticity. To overcome the defects of NMFsc, illumination estimation via nonnegative matrix factorization with dominant chromaticity image is proposed. First, image is converted to chromaticity color space and analyzed by chromaticity histogram. Chromaticity histogram segments the original image into similar chromaticity images. A segmented region with the lowest standard deviation is determined as dominant chromaticity region. Next, dominant chromaticity is removed in the original image. Then, illumination estimation using nonnegative matrix factorization is performed on the image without dominant chromaticity. To evaluate the proposed method, experimental results are analyzed by average angular error in the real world dataset and it has shown that the proposed method with 5.5 average angular error achieve better illuminant estimation over the previous method with 5.7 average angular error.

• Proceedings of the IEEK Conference
• /
• 2000.11d
• /
• pp.9-12
• /
• 2000

This paper proposes an improved image illumination estimation method based on the conventional color constancy algorithm. The most important process of color constancy algorithm is the estimation of the spectral distributions of illuminant of an input image. To estimate of the spectral distributions of illuminant of an input image, we use the brightest pixel values and the values of surface reflectance of an input image using a principal component analysis of the given munsell chips. We estimate a CIE tristimulus values of an input image using the estimated .spectral distribution of illuminant and recover an image by scaling it regularity. From the experimental results, the proposed method was effective in estimating the image illumination

• Journal of Korea Multimedia Society
• /
• v.25 no.8
• /
• pp.991-998
• /
• 2022

Recently, object recognition using image/video signals is rapidly spreading on autonomous driving and mobile phones. However, the actual input image/video signals are easily exposed to a poor illuminance environment. A recent researches for improving illumination enable to estimate and compensate the illumination parameters. In this study, we propose VE-DCE (video enhancement zero-reference deep curve estimation) to improve the illumination of low-light images. The proposed VE-DCE uses unsupervised learning-based zero-reference deep curve, which is one of the latest among learning based estimation techniques. Experimental results show that the proposed method can achieve the quality of low-light video as well as images compared to the previous method. In addition, it can reduce the computational complexity with respect to the existing method.

• Journal of Electrical Engineering and Technology
• /
• v.10 no.4
• /
• pp.1822-1829
• /
• 2015

In this study, artificial neural networks were used to determine the intensity of brightness in interior spaces. The illumination elements to illuminate indoor spaces were considered, not individually, but as a system. So, during the planned maintenance periods of an illumination system, after its design and installation, simple brightness level measurements must be taken. For a three-dimensional evaluation of the brightness level in indoor spaces in a speedy and accurate manner, the obtained brightness level measurement results and artificial neural network model were used. Upon estimation of the most suitable brightness level for indoor spaces by using the artificial neutral network model, the energy demands required by the illumination elements decreased. Consequently, in this study, with estimations of brightness levels, the extent to which the artificial neutral networks become successful was observed and more correct results have been obtained in terms of both economy and usage.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.13 no.1
• /
• pp.125-133
• /
• 2017

Color in an image is determined by illuminant and surface reflectance. So, to recover unique color of object, estimation of exact illuminant is needed. In this study, the illumination models suggested to get the object color constancy with the physical illumination model based on physical phenomena. Their characteristics and application limits are presented and the necessity of an extended illumination model is suggested to get more appropriate object colors recovered. The extended illumination model should contain an additional term for the ambient light in order to account for spatial variance of illumination in object images. Its necessity is verified through an experiment under simple lighting environment in this study. Finally, a reconstruction method for recovering input images under standard white light illumination is experimented and an useful method for computing object color reflectivity is suggested and experimented which can be induced from combination of the existing illumination models.

• Journal of KIISE:Software and Applications
• /
• v.33 no.1
• /
• pp.84-94
• /
• 2006

The color information in an image changes as the illuminant condition varies. The mechanism to find canonical color of an object by estimating illumination color in an image is generally referred as color constancy. In color constancy, computing robust and precise dichromatic line is most important to estimate illumination chromaticity. In this paper, a novel approach to estimate the color of a single illuminant for noisy and micro-textured images is introduced. An accurate dichromatic line is found by using Dichromatic Line Space (DLS), proposed in this paper. which has information about diffuse chromaticity and illumination chromaticity.

• Current Optics and Photonics
• /
• v.4 no.4
• /
• pp.317-325
• /
• 2020

Accurate estimation of spatial frequencies and phases for illumination patterns are essential to reconstructing super-resolution images in structured illumination microscopy (SIM). In this manuscript, we propose the improved component cross-correlation (ICC) algorithm, which is based on optimization of the cross-correlation values of the overlapping information between various spectral components. Compared to other algorithms for spatial-frequency and phase determination, the results calculated by the ICC algorithm are more accurate when the modulation depths of the illumination patterns are low. Moreover, the ICC algorithm is able to calculate the spatial frequencies and phases simultaneously. Simulation results indicate that even if the modulation depth is lower than 0.1, the ICC algorithm still estimates the parameters precisely; the images reconstructed by the ICC algorithm are much clearer than those reconstructed by other algorithms. In experiments, our home-built SIM system was used to image bovine pulmonary artery endothelial (BPAE) cells. Drawing support from the ICC algorithm, super-resolution images were reconstructed without artifacts.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.38 no.3
• /
• pp.292-303
• /
• 2001

Object color can be determined by the characteristic of scene illuminant and surface. In this paper, perceived illumination effect is extended and with the highlight analysis, hybrid approach is proposed to estimate the illuminant chromaticity. The perceived illumination approach provides a stable candidate range for the estimation of illuminant chromaticity, however, the accuracy is slightly degraded depending on the image contents. The highlight approach does not depend on the image contents and provides an accurate solution of the scene illuminant chromaticity, however, it is difficult to determine the final solution among many cross-points. These two approaches are in effect mutually compensating. The solution from perceived illumination can be used as a starting point or as base information for the highlight approach to get the final solution.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.11
• /
• pp.4170-4188
• /
• 2014

In this study, we present a denoising algorithm for high-frame-rate videos in an ultra-low illumination environment on the basis of Kalman filtering model and a new motion segmentation scheme. The Kalman filter removes temporal noise from signals by propagating error covariance statistics. Regarded as the process noise for imaging, motion is important in Kalman filtering. We propose a new motion estimation scheme that is suitable for serious noise. This scheme employs the small motion vector characteristic of high-frame-rate videos. Small changing patches are intentionally neglected because distinguishing details from large-scale noise is difficult and unimportant. Finally, a spatial bilateral filter is used to improve denoising capability in the motion area. Experiments are performed on videos with both synthetic and real noises. Results show that the proposed algorithm outperforms other state-of-the-art methods in both peak signal-to-noise ratio objective evaluation and visual quality.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.9
• /
• pp.738-744
• /
• 2016

In this paper, a robust visual odometry system has been proposed and implemented in an environment with dynamic illumination. Visual odometry is based on stereo images to estimate the distance to an object. It is very difficult to realize a highly accurate and stable estimation because image quality is highly dependent on the illumination, which is a major disadvantage of visual odometry. Therefore, in order to solve the problem of low performance during the feature detection phase that is caused by illumination variations, it is suggested to determine an optimal threshold value in the image binarization and to use an adaptive threshold value for feature detection. A feature point direction and a magnitude of the motion vector that is not uniform are utilized as the features. The performance of feature detection has been improved by the RANSAC algorithm. As a result, the position of a mobile robot has been estimated using the feature points. The experimental results demonstrated that the proposed approach has superior performance against illumination variations.