• The Journal of Korean Institute of Communications and Information Sciences
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• v.22 no.1
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• pp.193-204
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• 1997

In this research we propose the design of the Order Statistic-Adaptive Weighted Average Hybrid(OS-AWAH) filter which can suppress noise from the corrupted image sequence effectively while preserving the image structure. The proposed filter combines the desirable properties of the order static based spatial filter which can preserve the image structure while reducing noise and the adaptive weighted average based temporal filter which can adapt the filtering weights according to the amount of motion without motion estimation. Performance characteristics of the OS-AWAH filter in noisy sequences containing moving step edges are investigated throuth computer simulations and compared with the median based filters such as 3-D WM(weighted median) filter, MMF (multistage median filter), ADCWM(adaptive directional center weighted median) filter. The visual evaluations are also carried out by applyin gthe filters to the real images. The statistical analysis and experimental reslts show that the OS-AWAH filter is effective in preserving image structures while suppressing noise effectively without motion compensation preprocessing.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.4
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• pp.180-186
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• 2012

In this paper, a method of edge detection from low contrast and noisy images which contain hexagonal shape is proposed. This method is based on the combination of laplacian gaussian filter and an idea of filters which are dependent on the shape. First, an algorithm which has six masks as its extractors to detect the hexagonal edges especially in the comers is used. Here, two tricom filters are used to detect the tricom joints of hexagons and other four masks are used to enhance the line segments of hexagonal edges. As a natural image, a corneal endothelial cell image which usually has a regular hexagonal shape is selected. The edge detection of hexagonal shapes in this corneal endothelial cell is important for clinical diagnosis. Next, The proposal algorithm and other conventional methods are applied to noisy hexagonal images to evaluate each efficiency. As a result, this proposal algorithm shows a robustness against noises and better detection ability in the aspects of the signal to noise ratio, the edge coineidence ratio and the detection accuracy factor as compared with other conventional methods.

• Korean Journal of Remote Sensing
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• v.22 no.4
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• pp.275-284
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• 2006

The noise sources of a Hyperion image are mainly due to the atmospheric effects, the sensor's instrumental errors, and A/D conversion. Though uncalibrated, overlapping, and all deep water absorption bands generally are removed, there still exist noisy bands. The visual inspection for selecting clean and stable processing bands is a simple practice, but is a manual, inefficient, and subjective process. In this paper, we propose that the extrema ratio be used for noise estimation and unsupervised band selection. The extrema ratio was compared with existing SNR and entropy measures. First, Gaussian, salt and pepper, and Speckle noises were added to ALI (Advanced Land Imager) images with relatively low noises, and the relation of noise level and those measures was explored. Second, the unsupervised band selection was performed through the EM (Expectation-Maximization) algorithm of the measures which were extracted from a Hyperion images. The Hyperion data were classified into 5 categories according to the image quality by visual inspection, and used as the reference data. The experimental result showed that the extrema ratio could be used effectively for band selection of Hyperion images.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.479-490
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• 2003

This study proposed a modified anisotropic diffusion restoration for image classification. The anisotropic diffusion restoration uses a probabilistic model based on Markov random field, which represents geographical connectedness existing in many remotely sensed images, and restores them through an iterative diffusion processing. In every iteration, the bonding-strength coefficient associated with the spatial connectedness is adaptively estimated as a function of brightness gradient. The gradient function involves a constant called "temperature", which determines the amount of discontinuity and is continuously decreased in the iterations. In this study, the proposed method has been extensively evaluated using simulated images that were generated from various patterns. These patterns represent the types of natural and artificial land-use. The simulated images were restored by the modified anisotropic diffusion technique, and then classified by a multistage hierarchical clustering classification. The classification results were compared to them of the non-restored simulation images. The restoration with an appropriate temperature considerably reduces error in classification, especially for noisy images. This study made experiments on the satellite images remotely sensed on the Korean peninsula. The experimental results show that the proposed approach is also very effective on image classification in remote sensing.

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.719-731
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• 2021

This study proposes a two-stage hybrid classification model for crop classification using multi-temporal remote sensing images; the model combines feature embedding by using an autoencoder (AE) with a convolutional neural network (CNN) classifier to fully utilize features including informative temporal and spatial signatures. Long short-term memory (LSTM)-based AE (LAE) is fine-tuned using class label information to extract latent features that contain less noise and useful temporal signatures. The CNN classifier is then applied to effectively account for the spatial characteristics of the extracted latent features. A crop classification experiment with multi-temporal unmanned aerial vehicle images is conducted to illustrate the potential application of the proposed hybrid model. The classification performance of the proposed model is compared with various combinations of conventional deep learning models (CNN, LSTM, and convolutional LSTM) and different inputs (original multi-temporal images and features from stacked AE). From the crop classification experiment, the best classification accuracy was achieved by the proposed model that utilized the latent features by fine-tuned LAE as input for the CNN classifier. The latent features that contain useful temporal signatures and are less noisy could increase the class separability between crops with similar spectral signatures, thereby leading to superior classification accuracy. The experimental results demonstrate the importance of effective feature extraction and the potential of the proposed classification model for crop classification using multi-temporal remote sensing images.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.180-183
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• 2002

In this paper, we propose new unsharp masking technique based on the multiscale gradient planes. The unsharp masking technique is implemented as a high-pass filter and improves the sharpness of degraded images. However, the conventional unsharp masking enhances the noise component simultaneously. To reduce the noise influence, we introduce the edge information from the difference of the gradient values between two consecutive scales of the multiscale gradient. The multiscale gradient indicates the presence of image edges as the ratio between the gradients between two different scales by its multiscale nature. The noise reduction of the proposed method does not depend on the variance of images and noises. In experiment, we demonstrate enhancement results for blurred noisy images and compare with the conventional cubic unsharp masking technique.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.54-59
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• 1993

This paper presents a method of estimating 3-D surface geometrical features that are necessary for 3-D object recognition and image interpretation. The features, such as surface needle maps and curvatures, are computed from range or intensity images. In general, the range and intensity images are prone to noises, and hence the features computed by differentiation calculi on such a noisy image are hardly applicable to industrial recognition tasks. In our approach, we try to obtain a more accurate estimate of the features by using a least-squares minimization procedure subject to local curvature consistency constraints. The algorithm is robust with respect to noises and is completely independent of the viewpoint at which the image is taken. The performance of the ajgoritlim is evaluated using both synthetic data and real intensity images.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.246-249
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• 2009

Due to the additive white Gaussian noise (AWGN), images are often corrupted. In recent days, Bayesian estimation techniques to recover noisy images in the wavelet domain have been studied. The probability density function (PDF) of an image in wavelet domain can be described using highly-sharp head and long-tailed shapes. If a priori probability density function having the above properties would be applied well adaptively, better results could be obtained. There were some frequently proposed PDFs such as Gaussian, Laplace distributions, and so on. These functions model the wavelet coefficients satisfactorily and have its own of characteristics. In this paper, mixture distributions of Gaussian and Laplace distribution are proposed, which attempt to corporate these distributions' merits. Such mixture model will be used to remove the noise in images by adopting Maximum a Posteriori (MAP) estimation method. With respect to visual quality, numerical performance and computational complexity, the proposed technique gained better results.

• 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.

• Ocean Science Journal
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• v.40 no.4
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• pp.213-220
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• 2005

In this paper, image processing technique that reduces video images of buoy motion to yield time series of image coordinates of buoy objects will be investigated. The buoy motion images are noisy due to time-varying brightness as well as non-uniform background illumination. The occurrence of boats, wakes, and wind-induced white caps interferes significantly in recognition of buoy objects. Thus, semi-automated procedures consisting of object recognition and image measurement aspects will be conducted. These offer more satisfactory results than a manual process. Spectral analysis shows that the image coordinates of buoy objects represent wave motion well, indicating its usefulness in the analysis of wave characteristics.