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

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.8
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• pp.2866-2880
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• 2014

We propose a video denoising method based on Kalman filter to reduce the noise in video sequences. Firstly, with the strong spatiotemporal correlations of neighboring frames, motion estimation is performed on video frames consisting of previous denoised frames and current noisy frame based on intensity and structure tensor. The current noisy frame is processed in temporal domain by using motion estimation result as the parameter in the Kalman filter, while it is also processed in spatial domain using the Wiener filter. Finally, by weighting the denoised frames from the Kalman and the Wiener filtering, a satisfactory result can be obtained. Experimental results show that the performance of our proposed method is competitive when compared with state-of-the-art video denoising algorithms based on both peak signal-to-noise-ratio and structural similarity evaluations.

• Journal of IKEEE
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• v.23 no.4
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• pp.1295-1301
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• 2019

In this paper, we propose a novel method of denoising human motion using a bidirectional recurrent neural network (BRNN) with an attention mechanism. The corrupted motion captured from a single 3D depth sensor camera is automatically fixed in the well-established smooth motion manifold. Incorporating an attention mechanism into BRNN achieves better optimization results and higher accuracy than other deep learning frameworks because a higher weight value is selectively given to a more important input pose at a specific frame for encoding the input motion. Experimental results show that our approach effectively handles various types of motion and noise, and we believe that our method can sufficiently be used in motion capture applications as a post-processing step after capturing human motion.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.48 no.2
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• pp.24-31
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• 2011

This paper proposes spatio-temporal denoising algorithm based on nonlocal means. Though the conventional denoising algorithms based on nonlocal means have good performance in noise removal, it is difficult to implement them into the hardware system due to much computational load and the need for several frame buffers. Therefore we adopted infinite impulse response temporal noise reduction algorithm in the proposed algorithm. Proposed algorithm shows less artificial denoised result in the motionless region. In the motion region, spatial filter based on efficiently improved nonlocal means algorithm conduct noise removal with less motion blur. Experimental results including comparisons with conventional algorithms for various noise levels and test images show the proposed algorithm has a good performance in both visual and quantitative criteria.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.4
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• pp.61-67
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• 2017

Low-dose X-ray fluoroscopic image sequences to avoid radiation exposure risk are contaminated by quantum noise. To restore these noisy sequences, we propose a 3D nonlocal means (NLM) filter based on stochastic distancesed can be applied to the denoising of X-ray fluoroscopic image sequences. The stochastic distance is obtained within motion-compensated noise filtering support to remove the Poisson noise. In this paper, motion-adaptive weight which reflected the frame similarity is proposed to restore the noisy sequences without motion artifact. Experimental results including comparisons with conventional algorithms for real X-ray fluoroscopic image sequences show the proposed algorithm has a good performance in both visual and quantitative criteria.

• Journal of the Korean Society of Marine Environment & Safety
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• v.25 no.6
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• pp.809-818
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• 2019

Since January 1, 2020, the International Maritime Organization (IMO) has put in place strong regulations to reduce air pollution caused by ships by lowing the upper limit of ship fuel oil sulfur content from 3.5% to 0.5% for ships passing through all sea areas around the world. Although it is important to reduce air pollutants by using fuel oil with low sulfur content, reducing the amount of energy waste through the economic operation of a ship can also help reduce air pollutants. Ships can follow designated routes accurately even under the influence of noise using autopilot systems. However, regardless of their quality, the performance of these systems is af ected by noise; heading angles with added measurement noise from the gyroscope are input into the autopilot system and degrade its performance. A technique to solve these problems reduces noise effects through the application of a Kalman filter, which is widely used in condition estimation. This method, however, cannot completely eliminate the effects of noise. Therefore, to further improve noise removal performances, in this study we propose a better denoising method than the Kalman filter technique by applying a multi-layer perceptron (MLP) in forward direction motion and a Kalman Filter in rotation motion. Simulations show that the proposed method improves forward direction motion by preventing the malfunction of a rudder more so than merely using a Kalman Filter.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.10
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• pp.2205-2210
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• 2009

In this paper, the proposed method is a denoising technology by tangent curve interpolation of zero points. The problem of the hard threshold method is improved by the proposed method. The quantity of time fluctuation of the electromagnetic signal as the quantity of electric fluctuation of the natural world or the curve of motion waveform of the fast movement of human extracted using virtual reality is, in fact, complex. Therefore it is important to decide exactly the signal properties as the inflection point for observation signal. In particular, it is necessary to extract the properties after denoising, since the measurement signal of the natural world include some noises. It shows that the noise of the inflection point signal with noise II, noise factor 5, is eliminated by the proposed method, and the result of SNR for the signal is improved 3.4dB than that by the conventional hard threshold.

• Journal of Korea Multimedia Society
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• v.21 no.9
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• pp.1044-1051
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• 2018

In this paper, we propose a new method to improve low light image. In order to improve the image quality of a night image with a moving object as much as the quality of a daytime image, the following tasks were performed. Firstly, we reduce the noisy of the input night image and improve the night image by the tone mapping method. Secondly, we segment the input night image into a foreground with motion and a background without motion. The motion is detected using both the difference between the current frame and the previous frame and the difference between the current frame and the night background image. The background region of the night image takes pixels from corresponding positions in the daytime image. The foreground regions of the night image take the pixels from the corresponding positions of the image which is improved by the tone mapping method. Experimental results show that the proposed method can improve the visual quality more clearly than the existing methods.

• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.283-289
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• 2010

Recent advance of digital camera results in that image signal processing techniques are widely adopted to railroad security management. However, due to the nature of railroad management many images are acquired in low light level environment such as night scenes. The lack of light causes lots of noise in the image, which degrades image quality and causes errors in the next processes. 3D noise reducing techniques produce better results by using consecutive sequence of images. On the other hand, they cause degradation such as motion blur if there are motions in the sequence. In this paper, we use an adaptive weight filter to estimate more accurate motions and use the result of the adaptive filter to 3D result to improve objective and subjective mage quality.

• Journal of the Korea Computer Graphics Society
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• v.22 no.4
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• pp.1-9
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• 2016

In this paper we present a projection mapping system onto human's moving hand by a projector as information delivery media and Kinect to recognize hand motion. Most traditional projection mapping techniques project a variety of images onto stationary objects, however, our system provides new user experience by projecting images onto the center of the moving palm. We explain development process of the system, and production of content as applications on our system. We propose hardware organization and development process of open software architecture based on object oriented programming approach. For stable image projection, we describe a device calibration method between the projector and Kinect in three dimensional space, and a denoising technique to minimize artifacts from Kinect coordinates vibration and unstable hand tremor.