• 대한전기학회논문지
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• 제40권4호
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• pp.394-401
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• 1991

B-spline function is generally used for an image interpolation because of its smoothness and continuity, but it accompanies a large amount of blurring effect. In this paper, a space-variant B-spline interpolation function is proposed through deblurring process followed by de-aliasing process. The proposed function has parametric expression and performs smoothing and edge-enhancement adaptively in the interpolation process according to local property of the image. Application of this function to image enlargement, rotation, and curve representation producted good results. Even in the presence of noise, noise smoothing effect as well as edge-enhancement were observed in the image interpolation process.

• 방송공학회논문지
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• 제25권5호
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• pp.672-684
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• 2020

시차 기반 영상처리에 대한 연구들이 증가함에 따라 저해상도 및 모션 블러된 라이트필드 영상을 복원하는 연구는 필수적이 되었다. 이러한 기법들은 라이트필드 영상 향상 과정으로 알려져 있으나 두 개 이상의 문제를 동시에 해결하는 기존의 연구는 거의 존재하지 않는다. 본 논문에서는 라이트필드 공간 영역 초해상도 복원과 모션 블러 제거를 동시 수행하는 프레임워크를 제안한다. 특히, 저해상도 및 6-DOF 모션 블러된 라이트필드 데이터셋으로 훈련하는 간단한 네트워크를 생성한다. 또한 성능을 향상하기 위해 생성적 적대 신경망의 지역 영역 최적화 기법을 제안하였다. 제안한 프레임워크는 정량적, 정성적 측정을 통해 평가하고 기존의 state-of-the-art 기법들과 비교하여 우수한 성능을 나타냄을 보인다.

• 한국멀티미디어학회논문지
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• 제22권10호
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• pp.1149-1159
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• 2019

We present a novel deep learning architecture for obtaining a latent image from a single blurry image, which contains dynamic motion blurs through object/camera movements. The proposed architecture consists of two sub-modules: blur image restoration and optical flow estimation. The tasks are highly related in that object/camera movements make cause blurry artifacts, whereas they are estimated through optical flow. The ablation study demonstrates that training multi-task architecture simultaneously improves both tasks compared to handling them separately. Objective and subjective evaluations show that our method outperforms the state-of-the-arts deep learning based techniques.

• 충청수학회지
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• 제33권4호
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• pp.505-516
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• 2020

This study shows that image deblurring problems can be transformed into the multi-parameter Tikhonov type with multiple right hand sides. Also, this paper proposes the extension of the global generalized cross validation to obtain an appropriate choice of the regularization parameters for this problem. The experimental results of using the preconditioned Gl-CGLS algorithm were analyzed.

• Nuclear Engineering and Technology
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• 제55권12호
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• pp.4591-4596
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• 2023

Single-photon emission computed tomography SPECT image reconstruction methods have a significant influence on image quality, with filtered back projection (FBP) and ordered subset expectation maximization (OSEM) being the most commonly used methods. In this study, we proposed newly-designed adaptive non-blind deconvolution with a structural similarity (SSIM) index that can take advantage of the FBP and OSEM image reconstruction methods. After acquiring brain SPECT images, the proposed image was obtained using an algorithm that applied the SSIM metric, defined by predicting the distribution and amount of blurring. As a result of the contrast to noise ratio (CNR) and coefficient of variation evaluation (COV), the resulting image of the proposed algorithm showed a similar trend in spatial resolution to that of FBP, while obtaining values similar to those of OSEM. In addition, we confirmed that the CNR and COV values of the proposed algorithm improved by approximately 1.69 and 1.59 times, respectively, compared with those of the algorithm involving an inappropriate deblurring process. To summarize, we proposed a new type of algorithm that combines the advantages of SPECT image reconstruction techniques and is expected to be applicable in various fields.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2004년도 Proceedings of ISRS 2004
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• pp.115-118
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• 2004

Images with high resolution are desired and often required in many visual applications. When resolution can not be improved by replacing sensors, either because of cost or hardware physical limits, super resolution image reconstruction method is what can be resorted to. Super resolution image reconstruction method refers to image processing algorithms that produce high quality and high resolution images from a set of low quality and low resolution images. The method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, including satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. The method can be either the frequency domain approach or the spatial domain approach. Much of the earlier works concentrated on the frequency domain formulation, but as more general degradation models were considered, later researches had been almost exclusively on spatial domain formulations. The method in spatial domains has three stages: i) motion estimate or image registration, ii) interpolation onto high resolution grid and iii) deblurring process. The super resolution grid construction in the second stage was discussed in this paper. We applied the Maximum A­Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from a set of low resolution images and compared the results with those from other known interpolation methods.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권8호
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• pp.360-367
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• 2002

In this paper, genetic algorithm based adaptive image enhancement filtering scheme is proposed and Implemented on FPGA board. Conventional filtering methods require a priori noise information for image enhancement. In general, if a priori information of noise is not available, heuristic intuition or time consuming recursive calculations are required for image enhancement. Contrary to the conventional filtering methods, the proposed filter system can find optimal combination of filters as well as their sequent order and parameter values adaptively to unknown noise types using structured genetic algorithms. The proposed image enhancement filter system is mainly composed of two blocks. The first block consists of genetic algorithm part and fitness evaluation part. And the second block consists of four types of filters. The first block (genetic algorithms and fitness evaluation blocks) is implemented on host computer using C code, and the second block is implemented on re-configurabe FPGA board. For gray scale control, smoothing and deblurring, four types of filters(median filter, histogram equalization filter, local enhancement filter, and 2D FIR filter) are implemented on FPGA. For evaluation, three types of noises are used and experimental results show that the Proposed scheme can generate optimal set of filters adaptively without a pioi noise information.

• 전기전자학회논문지
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• 제19권2호
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• pp.148-154
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• 2015

차량 전장시스템은 매 순간마다 정확한 인식을 통하여 사용자에게 정확한 경보를 전달해야 한다. 따라서 차량 영상 인식 알고리즘을 적용하기 위하여 빠른 전처리 시스템이 필요하다. 본 논문은 운전자 보조 시스템의 영상 처리를 목적으로 histogram equalization과 편차를 이용한 bilateral Filter를 사용하여 blur 영상을 보정하는 방법에 대해서 제안하였다. 제안한 시스템은 영상 스케일, 평활화, 노이즈 필터, 윤곽선 추출 순으로 총 5단계로 구성되며, bilateral filter의 과 값을 운전자 보조 시스템에서 나타나는 도로의 주행 현상에 적합하게 추출하여 10픽셀 이하의 blur를 기존의 방법들보다 빠르게 처리하였다. 실험 결과는 MATLAB을 사용하여 소요시간 및 PSNR을 구하였으며 기존의 방법과 비교하여 본 논문의 결과가 처리속도가 빠름을 입증하였다.

• 전자공학회논문지B
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• 제31B권8호
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• pp.181-191
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• 1994

A new method for enhancing blurred images using fuzzy logic concepts is proposed. Blurred images contain blurred boundaries which make it difficult to detect edges and segment areas in images. In order to sharpen blurred edges local contrast information of an image and erosion/dilation properties of local min/max operations are used in which local min/max operations are fuzzy logic operations. so that given images are transformed to fuzzy images and then these operations are applied on them. In this method the sharpening operation can be iteratively applied to the image to get better deblurring effect and gray-scale "salt-and-pepper" noises are suppressed. the efficiency of our algorithm is demonstrated through experimental results obtained with artificially-made blurred images and real blurred images.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송∙미디어공학회 2020년도 하계학술대회
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• pp.680-683
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• 2020

오래 전부터 모델 기반 최적화 방법이 이미지 디블러링을 위해 널리 사용되어 왔고, 최근에는 학습 기반 기술이 영상 디블러링에서 좋은 성과를 보이고 있다. 본 논문은 ADMM과 깊은 합성곱 신경망 잡음 제거기 이미지 prior를 이용하여 모델 기반 최적화 방법의 장점과 학습 기반 방법의 장점을 모두 활용할 수 있는 방법을 제안한다. 본 방법을 이용하여 기존 방법보다 더 좋은 디블러링 성능을 얻을 수 있었다.