New Datasets, Models, and Optimization

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2021.8. 손현태.사진 촬영의 궁극적인 목표는 고품질의 깨끗한 영상을 얻는 것이다. 현실적으로, 일상의 사진은 자주 흔들린 카메라와 움직이는 물체가 있는 동적 환경에서 찍는다. 노출시간 중의 카메라와 피사체간의 상대적인 움직임은 사진과 동영상에서 모션 블러를 일으키며 시각적인 화질을 저하시킨다. 동적 환경에서 블러의 세기와 움직임의 모양은 매 이미지마다, 그리고 매 픽셀마다 다르다. 국지적으로 변화하는 블러의 성질은 사진과 동영상에서의 모션 블러 제거를 심각하게 풀기 어려우며 해답이 하나로 정해지지 않은, 잘 정의되지 않은 문제로 만든다. 물리적인 움직임 모델링을 통해 해석적인 접근법을 설계하기보다는 머신러닝 기반의 접근법은 이러한 잘 정의되지 않은 문제를 푸는 보다 현실적인 답이 될 수 있다. 특히 딥 러닝은 최근 컴퓨터 비전 학계에서 표준적인 기법이 되어 가고 있다. 본 학위논문은 사진 및 비디오 디블러링 문제에 대해 딥 러닝 기반 솔루션을 도입하며 여러 현실적인 문제를 다각적으로 다룬다. 첫 번째로, 디블러링 문제를 다루기 위한 데이터셋을 취득하는 새로운 방법을 제안한다. 모션 블러가 있는 이미지와 깨끗한 이미지를 시간적으로 정렬된 상태로 동시에 취득하는 것은 쉬운 일이 아니다. 데이터가 부족한 경우 디블러링 알고리즘들을 평가하는 것 뿐만 아니라 지도학습 기법을 개발하는 것도 불가능해진다. 그러나 고속 비디오를 사용하여 카메라 영상 취득 파이프라인을 모방하면 실제적인 모션 블러 이미지를 합성하는 것이 가능하다. 기존의 블러 합성 기법들과 달리 제안하는 방법은 여러 움직이는 피사체들과 다양한 영상 깊이, 움직임 경계에서의 가리워짐 등으로 인한 자연스러운 국소적 블러의 복잡도를 반영할 수 있다. 두 번째로, 제안된 데이터셋에 기반하여 새로운 단일영상 디블러링을 위한 뉴럴 네트워크 구조를 제안한다. 최적화기법 기반 이미지 디블러링 방식에서 널리 쓰이고 있는 점차적 미세화 접근법을 반영하여 다중규모 뉴럴 네트워크를 설계한다. 제안된 다중규모 모델은 비슷한 복잡도를 가진 단일규모 모델들보다 높은 복원 정확도를 보인다. 세 번째로, 비디오 디블러링을 위한 순환 뉴럴 네트워크 모델 구조를 제안한다. 디블러링을 통해 고품질의 비디오를 얻기 위해서는 각 프레임간의 시간적인 정보와 프레임 내부적인 정보를 모두 사용해야 한다. 제안하는 내부프레임 반복적 연산구조는 두 정보를 효과적으로 함께 사용함으로써 모델 파라미터 수를 증가시키지 않고도 디블러 정확도를 향상시킨다. 마지막으로, 새로운 디블러링 모델들을 보다 잘 최적화하기 위해 로스 함수를 제안한다. 깨끗하고 또렷한 사진 한 장으로부터 자연스러운 모션 블러를 만들어내는 것은 블러를 제거하는 것과 마찬가지로 어려운 문제이다. 그러나 통상 사용하는 로스 함수로 얻은 디블러링 방법들은 블러를 완전히 제거하지 못하며 디블러된 이미지의 남아있는 블러로부터 원래의 블러를 재건할 수 있다. 제안하는 리블러링 로스 함수는 디블러링 수행시 모션 블러를 보다 잘 제거하도록 설계되었다. 이에 나아가 제안한 자기지도학습 과정으로부터 테스트시 모델이 새로운 데이터에 적응하도록 할 수 있다. 이렇게 제안된 데이터셋, 모델 구조, 그리고 로스 함수를 통해 딥 러닝에 기반하여 단일 영상 및 비디오 디블러링 기법들을 제안한다. 광범위한 실험 결과로부터 정량적 및 정성적으로 최첨단 디블러링 성과를 증명한다.Obtaining a high-quality clean image is the ultimate goal of photography. In practice, daily photography is often taken in dynamic environments with moving objects as well as shaken cameras. The relative motion between the camera and the objects during the exposure causes motion blur in images and videos, degrading the visual quality. The degree of blur strength and the shape of motion trajectory varies by every image and every pixel in dynamic environments. The locally-varying property makes the removal of motion blur in images and videos severely ill-posed. Rather than designing analytic solutions with physical modelings, using machine learning-based approaches can serve as a practical solution for such a highly ill-posed problem. Especially, deep-learning has been the recent standard in computer vision literature. This dissertation introduces deep learning-based solutions for image and video deblurring by tackling practical issues in various aspects. First, a new way of constructing the datasets for dynamic scene deblurring task is proposed. It is nontrivial to simultaneously obtain a pair of the blurry and the sharp image that are temporally aligned. The lack of data prevents the supervised learning techniques to be developed as well as the evaluation of deblurring algorithms. By mimicking the camera image pipeline with high-speed videos, realistic blurry images could be synthesized. In contrast to the previous blur synthesis methods, the proposed approach can reflect the natural complex local blur from and multiple moving objects, varying depth, and occlusion at motion boundaries. Second, based on the proposed datasets, a novel neural network architecture for single-image deblurring task is presented. Adopting the coarse-to-fine approach that is widely used in energy optimization-based methods for image deblurring, a multi-scale neural network architecture is derived. Compared with the single-scale model with similar complexity, the multi-scale model exhibits higher accuracy and faster speed. Third, a light-weight recurrent neural network model architecture for video deblurring is proposed. In order to obtain a high-quality video from deblurring, it is important to exploit the intrinsic information in the target frame as well as the temporal relation between the neighboring frames. Taking benefits from both sides, the proposed intra-frame iterative scheme applied to the RNNs achieves accuracy improvements without increasing the number of model parameters. Lastly, a novel loss function is proposed to better optimize the deblurring models. Estimating a dynamic blur for a clean and sharp image without given motion information is another ill-posed problem. While the goal of deblurring is to completely get rid of motion blur, conventional loss functions fail to train neural networks to fulfill the goal, leaving the trace of blur in the deblurred images. The proposed reblurring loss functions are designed to better eliminate the motion blur and to produce sharper images. Furthermore, the self-supervised learning process facilitates the adaptation of the deblurring model at test-time. With the proposed datasets, model architectures, and the loss functions, the deep learning-based single-image and video deblurring methods are presented. Extensive experimental results demonstrate the state-of-the-art performance both quantitatively and qualitatively.1 Introduction 1 2 Generating Datasets for Dynamic Scene Deblurring 7 2.1 Introduction 7 2.2 GOPRO dataset 9 2.3 REDS dataset 11 2.4 Conclusion 18 3 Deep Multi-Scale Convolutional Neural Networks for Single Image Deblurring 19 3.1 Introduction 19 3.1.1 Related Works 21 3.1.2 Kernel-Free Learning for Dynamic Scene Deblurring 23 3.2 Proposed Method 23 3.2.1 Model Architecture 23 3.2.2 Training 26 3.3 Experiments 29 3.3.1 Comparison on GOPRO Dataset 29 3.3.2 Comparison on Kohler Dataset 33 3.3.3 Comparison on Lai et al. [54] dataset 33 3.3.4 Comparison on Real Dynamic Scenes 34 3.3.5 Effect of Adversarial Loss 34 3.4 Conclusion 41 4 Intra-Frame Iterative RNNs for Video Deblurring 43 4.1 Introduction 43 4.2 Related Works 46 4.3 Proposed Method 50 4.3.1 Recurrent Video Deblurring Networks 51 4.3.2 Intra-Frame Iteration Model 52 4.3.3 Regularization by Stochastic Training 56 4.4 Experiments 58 4.4.1 Datasets 58 4.4.2 Implementation details 59 4.4.3 Comparisons on GOPRO [72] dataset 59 4.4.4 Comparisons on [97] Dataset and Real Videos 60 4.5 Conclusion 61 5 Learning Loss Functions for Image Deblurring 67 5.1 Introduction 67 5.2 Related Works 71 5.3 Proposed Method 73 5.3.1 Clean Images are Hard to Reblur 73 5.3.2 Supervision from Reblurring Loss 75 5.3.3 Test-time Adaptation by Self-Supervision 76 5.4 Experiments 78 5.4.1 Effect of Reblurring Loss 78 5.4.2 Effect of Sharpness Preservation Loss 80 5.4.3 Comparison with Other Perceptual Losses 81 5.4.4 Effect of Test-time Adaptation 81 5.4.5 Comparison with State-of-The-Art Methods 82 5.4.6 Real World Image Deblurring 85 5.4.7 Combining Reblurring Loss with Other Perceptual Losses 86 5.4.8 Perception vs. Distortion Trade-Off 87 5.4.9 Visual Comparison of Loss Function 88 5.4.10 Implementation Details 89 5.4.11 Determining Reblurring Module Size 94 5.5 Conclusion 95 6 Conclusion 97 국문 초록 115 감사의 글 117박

Modeling of Foam Flow in Porous Media for Subsurface Environmental Remediation

Among numerous foam applications in a wide range of disciplines, foam flow in porous media has been spotlighted for improved/enhanced oil recovery processes in petroleum-bearing geological formations and shallow subsurface in-situ NAPL (non-aqueous phase liquid) environmental remediation in contaminated soils and aquifers. In those applications, foams are known to reduce the mobility of gas phase by increasing effective gas viscosity and improve sweep efficiency by mitigating subsurface heterogeneity. This study investigates how surfactant/foam process works fundamentally for environmental remediation purpose by using MoC (Method of Characteristics) based foam modeling and simulation techniques. It consists of two main parts: Part 1, developing foam model using three-phase fractional flow theory accounting for foam flow rheology such as foam strength and stability at different phase saturations; and Part 2, extending the model to investigate the mechanisms of surfactant/foam displacement in multi-layer systems. Part 1 investigates six scenarios such as different levels of foam strength (i.e., gas mobility reduction factors), different initial conditions (i.e., initially oil/water or oil/water/gas present), foam stability affected by water saturation (Sw), oil saturation (So), and both together, and uniform vs. non-uniform initial saturations. The process is analyzed by using ternary diagrams, fractional flow curves, effluent histories, saturation profiles, time-distance diagrams, and pressure and recovery histories. The results show that the three-phase fractional flow analysis presented in this study is robust enough to analyze foam-oil displacements in various conditions, as validated by an in-house numerical simulator built in this study. The use of numerical simulation seems crucial when the foam modeling becomes complicated and faces multiple possible solutions. Part 2 first shows how to interpret theoretically the injection of surfactant preflush and following foams into a single-layer system at pre-specified rock and fluid properties, and then extends the knowledge gained into multi-layer systems where the properties vary in different layers. The results in general show that the mechanisms of foam displacement strongly depend on foam properties such as gas-phase mobility reduction factors (MRF), limiting water saturation (Sw*), critical oil saturation (So*), and so on as well as petrophysical properties of individual layers such as porosity (φ), permeability (k), relative permeability and so on. The overall sweep efficiency in a multi-layer system is very difficult to predict because of the complexity, but the mathematical framework presented in this study is shown to be still reliable. The in-house foam simulator is also extended to compare with modeling results

Enhanced Deep Residual Networks for Single Image Super-Resolution

Recent research on super-resolution has progressed with the development of deep convolutional neural networks (DCNN). In particular, residual learning techniques exhibit improved performance. In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR methods. The significant performance improvement of our model is due to optimization by removing unnecessary modules in conventional residual networks. The performance is further improved by expanding the model size while we stabilize the training procedure. We also propose a new multi-scale deep super-resolution system (MDSR) and training method, which can reconstruct high-resolution images of different upscaling factors in a single model. The proposed methods show superior performance over the state-of-the-art methods on benchmark datasets and prove its excellence by winning the NTIRE2017 Super-Resolution Challenge.Comment: To appear in CVPR 2017 workshop. Best paper award of the NTIRE2017 workshop, and the winners of the NTIRE2017 Challenge on Single Image Super-Resolutio

The switching of rotaxane-based motors

Linear molecular motors have recently attracted considerable interest. In this paper we use molecular dynamics simulations to investigate the structural and energetic properties of neutral and oxidized [2]rotaxane motors. We first consider a neutral structure to identify the stable configuration of a bistable [2]rotaxane whose ring component is on the tetrathiafulvalene (TTF) recognition site, followed by a study of the dynamic switching process of an oxidized [2]rotaxane. The study shows that for a neutral structure the ring component stays at the TTF station in both free and constrained situations. When the station is oxidized, the ring is pushed to the other station and the dynamic switching process finishes in about 10 ns. By comparing the results for the cases of free and fixed ends, the simulations show that structural deformation plays an important role during the switching process and can significantly affect the displacement output of molecular motors.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/90788/1/0957-4484_22_20_205501.pd

Differential Co-Abundance Network Analyses for Microbiome Data Adjusted for Clinical Covariates Using Jackknife Pseudo-Values

A recent breakthrough in differential network (DN) analysis of microbiome data has been realized with the advent of next-generation sequencing technologies. The DN analysis disentangles the microbial co-abundance among taxa by comparing the network properties between two or more graphs under different biological conditions. However, the existing methods to the DN analysis for microbiome data do not adjust for other clinical differences between subjects. We propose a Statistical Approach via Pseudo-value Information and Estimation for Differential Network Analysis (SOHPIE-DNA) that incorporates additional covariates such as continuous age and categorical BMI. SOHPIE-DNA is a regression technique adopting jackknife pseudo-values that can be implemented readily for the analysis. We demonstrate through simulations that SOHPIE-DNA consistently reaches higher recall and F1-score, while maintaining similar precision and accuracy to existing methods (NetCoMi and MDiNE). Lastly, we apply SOHPIE-DNA on two real datasets from the American Gut Project and the Diet Exchange Study to showcase the utility. The analysis of the Diet Exchange Study is to showcase that SOHPIE-DNA can also be used to incorporate the temporal change of connectivity of taxa with the inclusion of additional covariates. As a result, our method has found taxa that are related to the prevention of intestinal inflammation and severity of fatigue in advanced metastatic cancer patients.Comment: 23 pages, 2 figures, 4 table