• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.35 no.4C
• /
• pp.408-414
• /
• 2010

When we are collecting traffic information on CCTV images, we have to install the detect zone in the image area during pan-tilt system is on duty. An automation of detect zone with pan-tilt system is not easy because of machine error. So the fisheye lens attached camera or convex mirror camera is needed for getting wide area images. In this situation some troubles are happened, that is a decreased system speed or image distortion. This distortion is caused by occlusion of angled ray as like trembled snapshot in digital camera. In this paper, we propose two methods of de-blurring to overcome distortion, the one is image segmentation by nonlinear diffusion equation and the other is deformation for some segmented area. As the results of doing de-blurring methods, the de-blurring image has 15 decibel increased PSNR and the detection rate of collecting traffic information is more than 5% increasing than in distorted images.

• The KIPS Transactions:PartA
• /
• v.13A no.5 s.102
• /
• pp.429-434
• /
• 2006

A CCTV system reduces some number of cameras if we use convex-mirror. In this case, convex-mirror Image distorted, we need transformation to flat images. In the center of mirror images, a transformed image has no distortion, but at near boundary image has plentiful distortion. This distortion is caused by occlusion of angled ray and diffraction. We know that the linear filtering approach cannot separate noise from signal where their Fourier spectra overlap. But using a non-linear discretization method, we shall reduce blurred noise. In this paper, we introduce the backward solution of nonlinear wave equation for reducing blurred noise and biased expansion of equilibrium contour. We propose, after applying the introduced method, and calculate with discretization method. To analysis the experimental result, we investigate to PSNR and get about 4dB better than current method.

• 한국HCI학회:학술대회논문집
• /
• 2009.02a
• /
• pp.27-33
• /
• 2009

We propose an efficient method for single image motion deblurring using edge prediction. Previous methods for motion deblurring from a single image have been based on total variation or natural image statistics. In contrast, our method predicts sharp edges by applying bilateral and shock filters and manipulating image gradients directly, and estimates motion blur using the predicted sharp edges. Sharp edge prediction makes our method possible to deblur efficiently with less computation. Results show that our method can effectively and efficiently restore images degraded by large complex motion blur.

• Journal of the Korea Society of Computer and Information
• /
• v.20 no.1
• /
• pp.103-110
• /
• 2015

In this paper, we propose a deblurring method using 1/4 selective filter. Deblurring methods require a lot of processing time for deblurring. In order to enhance execution speed, we propose a novel 1/4 selective filter. The proposed 1/4 selective filter restores major edge, but it distorts minor edge and texture. To solve this problem, we apply 1/4 selective filter to restore major edge and DOG(Difference of Gaussian) filter to restore minor edge and texture. Experimental results show that the proposed method removes the blur effectively.

• Journal of Broadcast Engineering
• /
• v.23 no.4
• /
• pp.484-494
• /
• 2018

The uniform motion blur removing algorithm of Cho et al. has the problem that the edge region of the image cannot be restored clearly. We propose the effective algorithm to overcome this problem by using shock filter that reconstructs a blurred step signal into a sharp edge, and convolutional neural network (CNN) that learns by extracting features from the image. Then uniform motion blur kernel is estimated from the latent sharp image to remove blur in the image. The proposed algorithm improved the disadvantages of the conventional algorithm by reconstructing the latent sharp image using shock filter and CNN. Through the experimental results, it was confirmed that the proposed algorithm shows excellent reconstruction performance in objective and subjective image quality than the conventional algorithm.

• Journal of radiological science and technology
• /
• v.45 no.2
• /
• pp.145-150
• /
• 2022

The main purpose of this work was to restore the blurry chest CT images by applying a blind deconvolution algorithm. In general, image restoration is the procedure of improving the degraded image to get the true or original image. In this regard, we focused on a blind deblurring approach with chest CT imaging by using digital image processing in MATLAB, which the blind deconvolution technique performed without any whole knowledge or information as to the fundamental point spread function (PSF). For our approach, we acquired 30 chest CT images from the public source and applied three type's PSFs for finding the true image and the original PSF. The observed image might be convolved with an isotropic gaussian PSF or motion blurring PSF and the original image. The PSFs are assumed as a black box, hence restoring the image is called blind deconvolution. For the 30 iteration times, we analyzed diverse sizes of the PSF and tried to approximate the true PSF and the original image. For improving the ringing effect, we employed the weighted function by using the sobel filter. The results was compared with the three criteria including mean squared error (MSE), root mean squared error (RMSE) and peak signal-to-noise ratio (PSNR), which all values of the optimal-sized image outperformed those that the other reconstructed two-sized images. Therefore, we improved the blurring chest CT image by using the blind deconvolutin algorithm for optimal approach.

• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.2
• /
• pp.83-90
• /
• 2023

Blurred image is an important factor in lowering image recognition rates in Computer vision. This mainly occurs when the camera is unstablely out of focus or the object in the scene moves quickly during the exposure time. Blurred images greatly degrade visual quality, weakening visibility, and this phenomenon occurs frequently despite the continuous development digital camera technology. In this paper, it replace the modified building module based on the Deep multi-patch neural network designed with convolution neural networks to capture details of input images and Attention techniques to focus on objects in blurred images in many ways and strengthen the image. It measures and assigns each weight at different scales to differentiate the blurring of change and restores from rough to fine levels of the image to adjust both global and local region sequentially. Through this method, it show excellent results that recover degraded image quality, extract efficient object detection and features, and complement color constancy.