• 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 Korea Institute of Information and Communication Engineering
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• v.26 no.11
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• pp.1637-1643
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• 2022

In recent years, the demand for image-processing technology in digital marketing has increased due to the expansion and diversification of the digital market, such as video, security, and machine intelligence. Noise-processing is essential for image-correction and reconstruction, especially in the case of sensitive noises, such as in CT, MRI, X-ray, and scanners. The two main salt and pepper noises have been actively studied, but the details and edges are still unsatisfactory and tend to blur when there is a lot of noise. Therefore, this paper proposes an algorithm that applies a weight-based Euclidean distance equation to the partial mask and uses only the non-noisy pixels that are the most similar to the original as effective pixels. The proposed algorithm determines the type of filter based on the state of the internal pixels of the designed partial mask and the degree of mask deterioration, which results in superior noise cancellation even in highly damaged environments.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.5 s.311
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• pp.1-11
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• 2006

This paper presents a digital auto-focusing method using a priori estimated point-spread-functions (PSF) database. The proposed algorithm efficiently removes out-of-focus blur in a degraded input image by selecting the optimal PSF from the database. The database consists of optical characteristics of image formation system. The PSF selection Process is performed based on a novel focusing measure. The proposed method includes a spatially adaptive filter for removing both noise and ringing artifacts. Experimental results show that the proposed method efficiently removes out-of-focus blur using significantly reduced computational load compared with the existing method.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.8 no.3
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• pp.105-112
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• 1983

This paper discribes technique of the recursive restoration for the images degraded by linear space invariant blur and additive white Gaussian noise. The image is characterized statistically by tis mean and correlation function. An exponential autocorrelation function has been used to model neighborhood model. The vector model was used because of analytical simplicitly and capability to implement brightness correlation function. Base on the vector model, a two-dimensional discrete stochastic a 12 point neighborhood model for represeting images was developme and used the technique of moving window processing to restore blurred and noisy images without dimensionality increesing, It has been shown a 12 point neighborhood model was found to be more adequate than a 8 point pixel model to obtain optimum pixel estimated. If the image is highly correlated, it is necessary to use a large number of points in the neighborhood in order to have improvements in restoring image. It is believed that these result could be applied to a wide range of image processing problem. Because image processing thchniques normally required a 2-D linear filtering.

• Journal of Biomedical Engineering Research
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• v.32 no.2
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• pp.85-92
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• 2011

Bioluminescence imaging (BLI) is the most sensitive animal imaging technique for molecular imaging research. Generally, highly sensitive CCD is used to detect an optical probe introduced in a living mouse. However, in many cases, the light signal emitted from a probe is too small to detect because it is scattered and attenuated by the tissue prior to being detected. The problem is that scattering and attenuation not only inhibit accurate measurement but also make image quality down. Thus we introduced a new method to reduce noise by using property of CCD and method to improve image quality of bioluminescence image by using two steps Gaussian blurring.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.3
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• pp.965-970
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• 2010

In this paper, a iterative image restoration method is proposed to restore for noisy-blurred images. In conventional method, regularization is usually applied to all over the without considering the local characteristics of image. As a result, ringing artifacts appear in edge regions and the noise amplification is introduced in flat regions. To solvethis problem we proposed an adaptive regularization iterative restoration using directional regularization operator considering edges in four directions and the regularization operator with no direction for flat regions. We verified that the proposed methods showed better results in the suppression of the noise amplification in flat regions, and introduced less ringing artifacts in edge regions. As a result it showed visually better image and improved better ISNR further than the conventional methods.

• Journal of Korea Multimedia Society
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• v.5 no.4
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• pp.399-404
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• 2002

To restore image degraded by motion blur and additive noise, it is very difficult. In conventional restoration method, regularization is usually applied to all over the image without considering the local characteristics of image. As a result, ringing artifacts appear in edge regions and the noise amplification is introduced in flat regions. To solve this problem we propose an adaptive restoration method using directional regularization operator considering edges and the regularization operator with no direction for flat regions. We verified that the proposed method showed better results in the resolution. As a result it showed visually better image and improved better ISNR further than the conventional methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.4005-4025
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• 2019

Copy-move forgery (CMF) in digital images is a detrimental tampering of artefacts that requires precise detection and analysis. CMF is performed by copying and pasting a part of an image into other portions of it. Despite several efforts to detect CMF, accurate identification of noise, blur and rotated region-mediated forged image areas is still difficult. A novel algorithm is developed on the basis of quaternion polar complex exponential transform (QPCET) to detect CMF and is conducted involving a few steps. Firstly, the suspicious image is divided into overlapping blocks. Secondly, invariant features for each block are extracted using QPCET. Thirdly, the duplicated image blocks are determined using k-dimensional tree (kd-tree) block matching. Lastly, a new technique is introduced to reduce the flat region-mediated false matches. Experiments are performed on numerous images selected from the CoMoFoD database. MATLAB 2017b is used to employ the proposed method. Metrics such as correct and false detection ratios are utilised to evaluate the performance of the proposed CMF detection method. Experimental results demonstrate the precise and efficient CMF detection capacity of the proposed approach even under image distortion including rotation, scaling, additive noise, blurring, brightness, colour reduction and JPEG compression. Furthermore, our method can solve the false match problem and outperform existing ones in terms of precision and false positive rate. The proposed approach may serve as a basis for accurate digital image forensic investigations.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.309-323
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• 1997

Although there are many photochemical images of which are censored while they are recorded, normal approaches are often applied to the restorations for them. In this case, it yields a restored image which might have serious bias. However, solutions for this problem are hardly found in the research of image restorations. This article provides a method of image restoration via EM algorithm for the censored images of which are contaminated with Gaussian noise and blur, also presents some results of simulation for artificial images censorized.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.26-27
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• 2012

Reflection considered as the view disturbing noise in optical systems, such as stereo camera in autonomous vehicles especially in night. Reflection caused by the street light or due to rainwater under adverse weather conditions. A blur image detected by the camera that results in wrong guidance to vehicle for detecting its track. A vehicle guidance approach through stereo vision can be same in day and night time. However it cannot be guided with same image analysis due to diverse illumination conditions. We develop the technique that shows its efficacy with illustrations of reflection removal off the camera lens and vehicle tracking control.