• Journal of the Institute of Convergence Signal Processing
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• v.11 no.3
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• pp.189-196
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• 2010

In this paper, we proposed the adaptive local sigma filter reducing noises generated by an image sensor. The small noises generated by the image sensor are amplified by increased an analog gain and an exposure time of the image sensor together with information. And the goal of this work was the system design that is reduce the these amplified noises. Edge data are extracted by Flatness Index Map algorithm. We made the threshold adaptively changeable by the luminance average in this algorithm that extracts the edge data not in high luminance, but just low luminance. The Local Sigma Filter performed only about the edge pixel that were extracted by Flatness Index Map algorithm. To verify the performance of the designed filter, we made the Window test program. The hardware was designed with HDL language. We verified the hardware performance of Local Sigma Filter system using FPGA Demonstration board and HD image sensor, $1280{\times}720$ image size and 30 frames per second.

• Journal of information and communication convergence engineering
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• v.9 no.1
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• pp.59-63
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• 2011

Conventional noise reduction filtering schemes realize limited improvements of the peak signal-to-noise ratio (PSNR) in the low-level noisy images. The flatness degree and the edge information are effectively used to estimate the noise volume. We propose a noise estimator for reducing noise in the AWGN (additive white gaussian noise) corrupted images using three intermediate image maps (FGM(flatness gray map), FIM(flatness index map), NEM(noise estimate map)). The proposed noise estimator is fed into the conventional noise reduction filters as a pre-processor. The performance of noise reduction is tested in the various AWGN corrupted images.

• Journal of the Institute of Convergence Signal Processing
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• v.21 no.3
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• pp.127-132
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• 2020

In this study, the process of providing information necessary to remove physical barriers such as road slopes that obstruct the activities of the disabled is in progress. Through experiments, we implement a quantified road surface roughness index that enables the implementation of IoT-based systems necessary for the elderly and the disabled to safely move to their destination. As a preliminary study, a road surface measurement device using a gyroscope was devised. To check the roughness and flatness of the road surface, X, Y displacement, and acceleration displacement were measured using a gyroscope. By calculating the measured data, the roughness and flatness of the road surface were quantified from 0 to 100. We implemented an algorithm that divides this index into 4 stages, displays it on a map, and provides it to users. Finally, a system for the disabled and elderly electric wheelchair users to secure basic mobility was established.