Scale-aware decomposition of images based on patch-based filtering

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2015. 2. 조남익.This dissertation presents an image decomposition algorithm based on patch-based filtering, for splitting an image into a structure layer and a texture layer. There are many applications through the decomposition because each layer can be processed respectively and appropriate manipulations are accomplished. Generally, structure layer captures coarse structure with large discontinuities and a texture layer contains fine details or proper patterns. The image decomposition is done by edge-preserving smoothing where structure layer can be obtained by applying smoothing filters to an image and then a texture layer by subtracting the filtered image from the original. The main contribution of this dissertation is to design an efficient and effective edge-preserving filter that can be adapted to various scales of images. The advantage of the proposed decomposition scheme is that it is robust to noise and can be extended to a noisy image decomposition, while conventional image decomposition methods cannot be applied to a noisy image decomposition and conventional image denoising methods are not suitable for image decomposition. To be specific, a patch-based framework is proposed in this dissertation, which is efficient in image denoising and it is designed to smooth an image while preserving details and texture. Specifically, given a pixel, the filtering output is computed as the weighted average of neighboring pixels. For computing the weights, a set of similar patches is found at each pixel by considering patch similarities based on mean squared error (MSE) and other constraints. Then, weights between each patch and its similar patches are computed respectively. With the patch weights, all the pixels in a patch are updated at the same time while adapting to the local pixel weight. For better edge-preserving smoothing, the proposed algorithm utilizes two iterations which are performed through the same smoothing filter with different parameters. Also kernel bandwidth and the number of similar patches are tuned for multi-scale image decomposition. The proposed decomposition can be applied to many applications, such as HDR tone mapping, detail enhancement, image denoising, and image coding, etc. In detail enhancement, the proposed smoothing filter is utilized to extract image detail and enhance it. In HDR tone mapping, a typical framework is used where the smoothing operator is replaced by the proposed one to reduce contrast range of a high dynamic range image to display it on low dynamic range devices. For image denoising, a noisy input is decomposed into structure/texture/noise and the noise layer is discarded while the texture layer is restored through the histogram matching. Also a novel coding scheme named as ``structure scalable image coding scheme'' is proposed where structure layer and salient texture layer are encoded for efficient image coding. Experimental results show that the proposed framework works well for image decomposition and it is robust to the presence of noise. Also it is verified that the proposed work can be utilized in many applications. In addition, by adopting the proposed method in decomposition of a noisy image, both image denoising and image enhancement can be achieved in the proposed framework. Furthermore, the proposed image coding method reduces compression artifact and improve the performance of image coding.Abstract i Contents iv List of Figures vi List of Tables xi 1 Introduction 1 1.1 Image decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Image enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Image denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1.3.1 Spatial denoising . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.3.2 Transformdomain denoising . . . . . . . . . . . . . . . . . . 9 1.3.3 benefits of combined image decomposition and image denoising 9 1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 2 Related work 17 2.1 Image decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 2.1.1 Laplacian subbands . . . . . . . . . . . . . . . . . . . . . . . 17 2.2 Edge-preserving smoothing . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.1 Bilateral filtering . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.2 Nonlocal means filtering . . . . . . . . . . . . . . . . . . . . . 21 3 Scale-aware decomposition of images based on patch-based filtering 23 3.1 Edge-preserving smoothing via patch-based framework . . . . . . . . 23 3.2 Multi-scale image decomposition . . . . . . . . . . . . . . . . . . . . 26 4 Applications 31 4.1 Image enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.1.1 Detail enhancement . . . . . . . . . . . . . . . . . . . . . . . 31 4.1.2 HDR tone mapping . . . . . . . . . . . . . . . . . . . . . . . 36 4.2 Image denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2.1 A noisy image decomposition . . . . . . . . . . . . . . . . . . 40 4.2.2 texture enhancement via histogram preservation . . . . . . . 41 4.2.3 image denoising via subband BLF . . . . . . . . . . . . . . . 44 4.2.4 Experimental results of image denoising . . . . . . . . . . . . 48 4.3 Image coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 4.3.1 Structure scalable image coding framework . . . . . . . . . . 61 5 Conclusion 73 Bibliography 76Docto

Development of novel infectious clones of genotype 1 hepatitis C virus

의과대학/석사Background & Aims: Hepatitis C virus (HCV) research has been hampered by the inability to culture HCV isolates in vitro except for the genotype 2a JFH1. Thus, it is important to develop cell culture-infectious clones for various HCV genotypes and to study cell culture-adaptive mutations which would enable development of infectious HCV clones. Among cell culture-adaptive mutations, S2204I robustly enhances HCV RNA replication in most HCV genotypes but the relevant mechanism is still unclear. Recent studies identified four cell culture-adaptive mutations F1464L/A1672S/D2979G/ Y2981F (LSGF) that permit many HCV isolates to be further adapted in cell culture. In this study, LSGF mutations were used as the first step for developing genotype 1 infectious clones and we also investigated the effect of S2204I mutation on viral RNA replication. Method: Multiple cell culture-adaptive mutations including LSGF were introduced into genotype 1a and 1b HCV sequences. We also generated various recombinant chimeras with or without S2204I mutation. And we generated genotype 1a constructs including substitutions at position 2204 (threonine and serine) which were found in the chimpanzee-infection experiment. Huh7.5 cells were transfected with in vitro-transcribed viral RNA and HCV RNA replication was evaluated by Gaussia luciferase reporter assay. Results: LSGF mutations did not confer the RNA replication ability of H77C and H77S.3 (genotype 1a). Con1 (genotype 1b) that was introduced with multiple cell culture-adaptive mutations including LSGF showed a low RNA replication level. The RNA replication of H77S-based JFH1 chimeras in transfected cells was impaired regardless of S2204I mutation. RNA replication level of H77S.3 with either threonine or serine at position 2204 was similar to each other. Conclusion: LSGF mutations did not increase RNA replication capacities of H77C, H77S.3 and Con1. Capability of S2204I mutation in enhancing HCV RNA replication was not dependent on background genotypes. Serine and threonine at position 2204 supported HCV RNA replication similarly in in-vitro transfection experiments.ope

Image Registration Using Image Segmentation and Mesh-based Image Transformation

학위논문(석사) --서울대학교 대학원 :전기. 컴퓨터공학부,2010.2.Maste

자율주행자동차에 대한 소비자 선호 연구: 혼합 로짓 모형을 이용하여

학위논문 (석사)-- 서울대학교 대학원 : 협동과정 기술경영·경제·정책전공, 2014. 8. 이종수.현재 자동차 산업은 19세기 산업혁명으로 인해 자동차가 등장한 이래 새로운 시기의 전환점을 맞고 있다. 자동차 기술과 IT 기술의 접목을 통해 이전에 없던 자동차 산업의 시대가 도래할 것으로 예상되고 있다. IT 기술과 자동차 기술이 접목된 기술 중 하나인 자율주행자동차는 앞으로자동차를 둘러싼 사회적 환경을 크게 변화시킬 전망이다. 자율주행자동차가 상용화가 될 경우 운전자의 편의를 향상시킬 뿐만 아니라 교통 효율성을 높이며, 교통사고가 감소하는 등, 현재 자동차로 인한 사회적 비용을 크게 감소시킬 수 있을 것이라 예상된다. 그러나 앞으로 자율주행자동차가 상용화 되기에는 넘어야 할 장벽이 높다. 자율주행자동차 기술의 발전 현황과는 별개로 상용화를 위한 도로 및 관련 인프라나 법적인 제도 구축이 미비하며, 자동차의 시스템 오류 확률 및 안전성에 대한 입증이 아직 부족하기 때문이다. 이러한 시점에서 자율주행자동차의 구매에 영향을 미치는 요인이 무엇인지에 대한 논의는 거의 없는 실정이다. 본 연구에서는 이러한 문제들에 대한 정보를 제공하기 위해 혼합 로짓 모형(Mixed Logit Model)을 이용하여 자율주행자동차에 대한 소비자의 선호를 분석하고자 한다. 소비자들의 자율주행자동차의 구매에 영향을 미치는 요인들에 대해 논의하며, 향후 소비자들의 자율주행자동차 선택이 자율주행자동차를 구성하는 속성 변화에 따라 어떻게 변화하는지를 분석하고자 한다.제 1장 서론 제 2장 이론적 배경 및 선행 연구 2.1 자동차에 대한 소비자 선호 분석 연구 2.2 대체연료자동차에 대한 수요 예측 연구 2.3 본 연구의 의의 제 3장 연구 모형 및 방법론 3.1 연구 설계 3.2 실증 모형 제 4장 실증 분석 4.1 자료의 구성 4.2 추정결과 및 결과분석 4.2.1 추정 결과 4.2.2 상대적 중요도 4.2.3 한계지불의사액 4.2.4 직접탄력성 4.2.5 민감도 분석 4.2.6 자율주행자동차 인프라 재원 마련 방안 분석 제 5장 결론 및 시사점 참고문헌 부록: 설문지Maste