• The Journal of Korea Robotics Society
• /
• v.18 no.3
• /
• pp.271-280
• /
• 2023

FMCW (Frequency Modulated Continuous Wave) radar system is widely used in autonomous driving and navigation applications due to its high detection capabilities independent of weather conditions and environments. However, radar signals can be easily contaminated by various noises such as speckle noise, receiver saturation, and multipath reflection, which can worsen sensing performance. To handle this problem, we propose a learning-free noise removal technique for radar to enhance detection performance. The proposed method leverages adaptive thresholding to remove speckle noise and receiver saturation, and wavelet transform to detect multipath reflection. After noise removal, the radar image is reconstructed with the geometric structure of the surrounding environments. We verify that our method effectively eliminated noise and can be applied to autonomous driving by improving the accuracy of odometry and place recognition.

• Korean Journal of Remote Sensing
• /
• v.12 no.2
• /
• pp.155-168
• /
• 1996

Speckle noise has been a primary concern to many applications of synthetic aperture radar (SAR) imagery. In recent years, several satellites with radar imaging systems were launched and the use of SAR data are expected to be increased rapidly The objectives of this study are to provide introductory understanding on radar speckle filtering and to compare the effects of several filtering methods that are relatively unknown to user community. Two study sites were extracted from the RADARSAT SAR data obtained over the suburban areas near Seoul. The study sites include relatively homogeneous cover types, such as reservoir, parking lot, rice pad, and deciduous forest. Five filters (mean filter, median filter, sigma filter, local statistics filter, and autocorrelation filter) were applied to the SAR imagery and their effects were evaluated from the aspects of both image smoothing and edge preservation. In overall, the evaluation results indicate that the local statistics filter and autocorrelation filter, that are based on a speckle model, are more effective to suppress speckle within homogeneous cover type while maintaining the edge sharpness between cover types.

• Korean Journal of Optics and Photonics
• /
• v.34 no.5
• /
• pp.192-201
• /
• 2023

The band-limited angular spectrum method (BL-ASM) causes aliasing errors due to spatial frequency control problems. In this paper, a sampling interval adjustment technique for phase holograms and a technique for reducing speckle noise and improving image quality using a deep-learningbased U-net model are proposed. With the proposed technique, speckle noise is reduced by first calculating the sampling factor and controlling the spatial frequency by adjusting the sampling interval so that aliasing errors can be removed in a wide range of propagation. The next step is to improve the quality of the reconstructed image by learning the phase hologram to which the deep learning model is applied. In the S/W simulation of various sample images, it was confirmed that the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) were improved by 5% and 0.14% on average, compared with the existing BL-ASM.

• Proceedings of the Optical Society of Korea Conference
• /
• 2009.10a
• /
• pp.329-330
• /
• 2009

• Journal of information and communication convergence engineering
• /
• v.7 no.2
• /
• pp.231-234
• /
• 2009

Speckle noise reduction for power Doppler ventricle coherent image for restoration and enhancement using Fast Wavelet Transform with multi-thresholding and multi-filtering on the each subbands is presented. Fast Wavelet Transform divides into low frequency component image to high frequency component image to be multi-resolved. Speckle noise is located on high frequency component in multi-resolution image mainly. A Doppler ventricle image is transformed and inversed with separated threshold function and filtering from low to high resolved images for restoration to utilize visualization for ventricle diagnosis. The experimental result shows that the proposed method has better performance in comparison with the conventional method.

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.20 no.1
• /
• pp.37-44
• /
• 2016

Ultrasonic imaging is the most widely modality among modern imaging device for medical diagnosis. Nevertheless, medical ultrasound images suffer from speckle noise and low contrast. In this paper, we propose probabilistic edge map for ultrasound image edge enhancement using automatic alien algorithm. The proposed method used applied speckle reduced ultrasound imaging for edge improvement using sequentially acquired ultrasound imaging. To evaluate the performance of method, the similarity between the reference and edge enhanced image was measured by quantity analysis. The experimental results show that the proposed method considerably improves the image quality with region edge enhancement.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.270-273
• /
• 2007

In this paper, a SAR-despeck1ing approach of adaptive iteration based a Bayesian model using the lognormal distribution for image intensity and a Gibbs random field (GRF) for image texture is proposed for noise removal of the images that are corrupted by multiplicative speckle noise. When the image intensity is logarithmically transformed, the speckle noise is approximately Gaussian additive noise, and it tends to a normal probability much faster than the intensity distribution. The MRF is incorporated into digital image analysis by viewing pixel types as states of molecules in a lattice-like physical system. The iterative approach based on MRF is very effective for the inner areas of regions in the observed scene, but may result in yielding false reconstruction around the boundaries due to using wrong information of adjacent regions with different characteristics. The proposed method suggests an adaptive approach using variable parameters depending on the location of reconstructed area, that is, how near to the boundary. The proximity of boundary is estimated by the statistics based on edge value, standard deviation, entropy, and the 4th moment of intensity distribution.

• Journal of Sensor Science and Technology
• /
• v.13 no.2
• /
• pp.121-127
• /
• 2004

A reflection type optical fiber line sensor based on the speckle detection has been developed using the optical fiber mirror with the reflectance of 72 %. Some experiments were carried on with the automobile and the 500 m length multimod fiber. When the automobile passed on the fiber sensor, two abrupt changes of the output waveform occured like the transmission type sensor based on the speckle variations. The signal to noise ratio of this sensor was about 40 dB.

• Korean Journal of Optics and Photonics
• /
• v.25 no.2
• /
• pp.85-89
• /
• 2014

In this paper, we present a speckle-free digital hologram with conversion to an off-axis horizontal-parallax-only (HPO) hologram. First, we record the speckle-free hologram using optical scanning holography. Second, we digitally convert the full-parallax hologram to a horizontal-parallax-only hologram. Third, we convert the horizontal-parallax-only hologram to an off-axis hologram. Finally, we show that the off-axis HPO hologram can be numerically reconstructed in space.

• Journal of IKEEE
• /
• v.12 no.3
• /
• pp.158-166
• /
• 2008

Medical ultrasound has benefits in mobility and safety than any other medical techniques such as X-ray, CT and MRI but has speckle noise which decrease the ability of an observer to distinguish the fine details in diagnostic examination. But simple removing of speckle often causes losing boundary information. Then, in this paper, we presented a novel neuro-fuzzy method which could remove speckle efficiently without loss of boundary information. Proposed method consists of image clustering by fuzzy algorithm and image processingby neural networks which was learned by back propagation. From the experiments for simulation image and real ultrasound image, we could verify the proposed method.