• Current Optics and Photonics
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• v.8 no.3
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• pp.215-224
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• 2024

In medium- and long-range optical imaging systems, atmospheric turbulence causes blurring and distortion of images, resulting in loss of image information. An image-restoration method based on an adaptive feature-fusion multi-input-multi-output (MIMO) dense U-shaped network (Unet) is proposed, to restore a single image degraded by atmospheric turbulence. The network's model is based on the MIMO-Unet framework and incorporates patch-embedding shallow-convolution modules. These modules help in extracting shallow features of images and facilitate the processing of the multi-input dense encoding modules that follow. The combination of these modules improves the model's ability to analyze and extract features effectively. An asymmetric feature-fusion module is utilized to combine encoded features at varying scales, facilitating the feature reconstruction of the subsequent multi-output decoding modules for restoration of turbulence-degraded images. Based on experimental results, the adaptive feature-fusion MIMO dense U-shaped network outperforms traditional restoration methods, CMFNet network models, and standard MIMO-Unet network models, in terms of image-quality restoration. It effectively minimizes geometric deformation and blurring of images.

• Journal of Electrical Engineering and Technology
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• v.10 no.4
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• pp.1886-1898
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• 2015

In today’s era of advanced technological developments, the threats to the authenticity and integrity of digital images, in a nutshell, the threats to the Image Forensics Research communities have also increased proportionately. This happened as even for the ‘non-expert’ forgers, the availability of image processing tools has become a cakewalk. This image forgery poses a great problem for judicial authorities in any context of trade and commerce. Block matching based image cloning detection system is widely researched over the last 2-3 decades but this was discouraged by higher computational complexity and more time requirement at the algorithm level. Thus, for reducing time need, various dimension reduction techniques have been employed. Since a single technique cannot cope up with all the transformations like addition of noise, blurring, intensity variation, etc. we employ multiple techniques to a single image. In this paper, we have used Fuzzy logic approach for decision making and getting a global response of all the techniques, since their individual outputs depend on various parameters. Experimental results have given enthusiastic elicitations as regards various transformations to the digital image. Hence this paper proposes Fuzzy based cloning detection and classification system. Experimental results have shown that our detection system achieves classification accuracy of 94.12%. Detection accuracy (DAR) while in case of 81×81 sized copied portion the maximum accuracy achieved is 99.17% as regards subjection to transformations like Blurring, Intensity Variation and Gaussian Noise Addition.

• Convergence Security Journal
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• v.22 no.4
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• pp.141-146
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• 2022

The iris, a biometric information, is safe, unique, and reliable, such as fingerprints, and is personal information that can significantly lower the misrecognition rate than other biometric authentication. However, due to the nature of biometric authentication, it is impossible to replace it if it is stolen. There is a case in which an actual iris photo is taken and 3d printed so that the eyes work as if they were in front of the camera. As such, there is a possibility of iris leakage through high-definition images and photos. In this paper, we propose to improve iris masking performance by supplementing iris region masking research based on existing blurring techniques. Based on the results derived in this study, it is expected that it can be used for the security of video conference programs and electronic devices.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1745-1753
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• 2004

A method of estimating the pose of a three-dimensional object from a set of two-dimensioal images based on parametric eigenspace method is proposed. A Gaussian blurred edge image is used as an input image instead of the original image itself as has been used previously. The set of input images is compressed using K-L transformation. By comparing the estimation errors for the original, blurred original, edge, and blurred edge images, we show that blurring with the Gaussian function and the use of edge images enhance the data compression ratio and decrease the resulting from smoothing the trajectory in the parametric eigenspace, thereby allowing better pose estimation to be achieved than that obtainable using the original images as it is. The proposed method is shown to have improved efficiency, especially in cases with occlusion, position shift, and illumination variation. The results of the pose angle estimation show that the blurred edge image has the mean absolute errors of the pose angle in the measure of 4.09 degrees less for occlusion and 3.827 degrees less for position shift than that of the original image.

• ETRI Journal
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• v.46 no.2
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• pp.165-174
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• 2024

We propose a method to suppress the speckle noise and blur effects of the light field extracted from a hologram using a deep-learning technique. The light field can be extracted by bandpass filtering in the hologram's frequency domain. The extracted light field has reduced spatial resolution owing to the limited passband size of the bandpass filter and the blurring that occurs when the object is far from the hologram plane and also contains speckle noise caused by the random phase distribution of the three-dimensional object surface. These limitations degrade the reconstruction quality of the hologram resynthesized using the extracted light field. In the proposed method, a deep-learning model based on a generative adversarial network is designed to suppress speckle noise and blurring, resulting in improved quality of the light field extracted from the hologram. The model is trained using pairs of original two-dimensional images and their corresponding light-field data extracted from the complex field generated by the images. Validation of the proposed method is performed using light-field data extracted from holograms of objects with single and multiple depths and mesh-based computer-generated holograms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.8
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• pp.453-458
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• 2014

In this paper, we propose a stereo image composition method based on Poisson image editing. If we synthesize images without considering their depth values, it may lead to unwanted consequences. When we segment an image into its background and foreground regions using Grabcut, we take into account their geometric positions to mix color tones; thus, the image is composited more naturally. After synthesizing images, we apply a blurring operation around object boundaries; then, the foreground object and background are composited more seamlessly. In addition, we can adjust the distance of the object by setting arbitrary depth values and generating right color and depth images automatically. Experimental results show that the proposed stereo image composition method provides naturally synthesized stereo images. Improved portions were subjectively confirmed as well.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.572-576
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• 1997

X-ray laminography and DT(digital tomosynthesis) that can form a cross-sectional image of 3-D objects promise to be good solutions for inspecting interior defects of industrial products. The major factors of the digital tomosynthesis that influence on the quality of x-ray cross-sectional images are also discussed. The quality of images acquired from the DT system varies according to image synthesizing methods, the number of images used in image synthesizing, and X-ray projection angles. In this paper, a new image synthesizing method named 'log-root method' is proposed to get clear and accurate cross-sectional images, which can reduce both artifact and blurring generated by materials out of focal plane. To evaluate the quality of cross-sectional images, two evaluating criteria: (1) shape accuracy and (2) clearness in the cross-sectional image are defined. Based on this criteria, a series of simulations were performed, and the results show the superiority of the new synthesizing method over the existing ones such as averaging and minimum method.

• Journal of Biomedical Engineering Research
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• v.12 no.4
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• pp.295-302
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• 1991

In this paper, the inter- and intra-frame distortions in the gray levels of a series of fluorescein ocular fundus photographs are corrected. For doing this, the background images are extracted from original images using the image blurring effect by decimation, and then shading corrected images are obtained by subtracting the background images from the original images pixel by pixel. In a series of fluorescein ocular fundus photographs, after the gray scale distoriton is corrected, the intensity volumes of dye leakage are measured and represented by a graph. These data may be useful for the prediction of prognosis and the therapeutic management.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.234-237
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• 2000

A number of theoretical researches have been done in recent years on the restoration of images and a variety of algorithms have been developed to implement noise reduction methods. However the blurring effect has not been perfectly overcome in the process of noise reduction. In this paper, we propose a new approach to image restoration that the blurring effect is significantly decreased and the performance of the noise reduction improves by eliminating the noise in the wavelet transform domain in comparison with the conventional noise reduction methods. The proposed algorithm performs much better than the conventional in the subjective image quality and PSNR performance. It is verified through computer simulations,

• 전기의세계
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• v.31 no.3
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• pp.204-208
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• 1982

The objective of this paper is to present a digital method for improving the lateral resolution of the B-scan images in the medical applications of ultrasound. The method is based upon a mathematical model of the lateral blurring caused by the finite beam width of the transducers. This model provides a simple method of applying a recursive scheme for image restoration with fast computation time. The point spread function (P.S.F.) can be measured by the reflective signals after scanning the small pins located along the depth of interest. From the measured P.S.F., one can compute the coefficient matrices of the inverse discrete-time dynamic state variable equation of the blurring process. Then, a recursive scheme for deblurring is applied to the recorded B-scan to improve the lateral resolution. One major advantage of the present recursive scheme over the transform method is in its applicability for the space-variant imaging, such as in the case of the rotational movement of transducer.