특징 혼합 네트워크를 이용한 영상 정합 기법과 고 명암비 영상법 및 비디오 고 해상화에서의 응용

학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2020. 8. 조남익.This dissertation presents a deep end-to-end network for high dynamic range (HDR) imaging of dynamic scenes with background and foreground motions. Generating an HDR image from a sequence of multi-exposure images is a challenging process when the images have misalignments by being taken in a dynamic situation. Hence, recent methods first align the multi-exposure images to the reference by using patch matching, optical flow, homography transformation, or attention module before the merging. In this dissertation, a deep network that synthesizes the aligned images as a result of blending the information from multi-exposure images is proposed, because explicitly aligning photos with different exposures is inherently a difficult problem. Specifically, the proposed network generates under/over-exposure images that are structurally aligned to the reference, by blending all the information from the dynamic multi-exposure images. The primary idea is that blending two images in the deep-feature-domain is effective for synthesizing multi-exposure images that are structurally aligned to the reference, resulting in better-aligned images than the pixel-domain blending or geometric transformation methods. Specifically, the proposed alignment network consists of a two-way encoder for extracting features from two images separately, several convolution layers for blending deep features, and a decoder for constructing the aligned images. The proposed network is shown to generate the aligned images with a wide range of exposure differences very well and thus can be effectively used for the HDR imaging of dynamic scenes. Moreover, by adding a simple merging network after the alignment network and training the overall system end-to-end, a performance gain compared to the recent state-of-the-art methods is obtained. This dissertation also presents a deep end-to-end network for video super-resolution (VSR) of frames with motions. To reconstruct an HR frame from a sequence of adjacent frames is a challenging process when the images have misalignments. Hence, recent methods first align the adjacent frames to the reference by using optical flow or adding spatial transformer network (STN). In this dissertation, a deep network that synthesizes the aligned frames as a result of blending the information from adjacent frames is proposed, because explicitly aligning frames is inherently a difficult problem. Specifically, the proposed network generates adjacent frames that are structurally aligned to the reference, by blending all the information from the neighbor frames. The primary idea is that blending two images in the deep-feature-domain is effective for synthesizing frames that are structurally aligned to the reference, resulting in better-aligned images than the pixel-domain blending or geometric transformation methods. Specifically, the proposed alignment network consists of a two-way encoder for extracting features from two images separately, several convolution layers for blending deep features, and a decoder for constructing the aligned images. The proposed network is shown to generate the aligned frames very well and thus can be effectively used for the VSR. Moreover, by adding a simple reconstruction network after the alignment network and training the overall system end-to-end, A performance gain compared to the recent state-of-the-art methods is obtained. In addition to each HDR imaging and VSR network, this dissertation presents a deep end-to-end network for joint HDR-SR of dynamic scenes with background and foreground motions. The proposed HDR imaging and VSR networks enhace the dynamic range and the resolution of images, respectively. However, they can be enhanced simultaneously by a single network. In this dissertation, the network which has same structure of the proposed VSR network is proposed. The network is shown to reconstruct the final results which have higher dynamic range and resolution. It is compared with several methods designed with existing HDR imaging and VSR networks, and shows both qualitatively and quantitatively better results.본 학위논문은 배경 및 전경의 움직임이 있는 상황에서 고 명암비 영상법을 위한 딥 러닝 네트워크를 제안한다. 움직임이 있는 상황에서 촬영된 노출이 다른 여러 영 상들을 이용하여 고 명암비 영상을 생성하는 것은 매우 어려운 작업이다. 그렇기 때문에, 최근에 제안된 방법들은 이미지들을 합성하기 전에 패치 매칭, 옵티컬 플로우, 호모그래피 변환 등을 이용하여 그 이미지들을 먼저 정렬한다. 실제로 노출 정도가 다른 여러 이미지들을 정렬하는 것은 아주 어려운 작업이기 때문에, 이 논문에서는 여러 이미지들로부터 얻은 정보를 섞어서 정렬된 이미지를 합성하는 네트워크를 제안한다. 특히, 제안하는 네트워크는 더 밝게 혹은 어둡게 촬영된 이미지들을 중간 밝기로 촬영된 이미지를 기준으로 정렬한다. 주요한 아이디어는 정렬된 이미지를 합성할 때 특징 도메인에서 합성하는 것이며, 이는 픽셀 도메인에서 합성하거나 기하학적 변환을 이용할 때 보다 더 좋은 정렬 결과를 갖는다. 특히, 제안하는 정렬 네트워크는 두 갈래의 인코더와 컨볼루션 레이어들 그리고 디코더로 이루어져 있다. 인코더들은 두 입력 이미지로부터 특징을 추출하고, 컨볼루션 레이어들이 이 특징들을 섞는다. 마지막으로 디코더에서 정렬된 이미지를 생성한다. 제안하는 네트워크는 고 명암비 영상법에서 사용될 수 있도록 노출 정도가 크게 차이나는 영상에서도 잘 작동한다. 게다가, 간단한 병합 네트워크를 추가하고 전체 네트워크들을 한 번에 학습함으로서, 최근에 제안된 방법들 보다 더 좋은 성능을 갖는다. 또한, 본 학위논문은 동영상 내 프레임들을 이용하는 비디오 고 해상화 방법을 위한 딥 러닝 네트워크를 제안한다. 동영상 내 인접한 프레임들 사이에는 움직임이 존재하기 때문에, 이들을 이용하여 고 해상도의 프레임을 합성하는 것은 아주 어려운 작업이다. 따라서, 최근에 제안된 방법들은 이 인접한 프레임들을 정렬하기 위해 옵티컬 플로우를 계산하거나 STN을 추가한다. 움직임이 존재하는 프레임들을 정렬하는 것은 어려운 과정이기 때문에, 이 논문에서는 인접한 프레임들로부터 얻은 정보를 섞어서 정렬된 프레임을 합성하는 네트워크를 제안한다. 특히, 제안하는 네트워크는 이웃한 프레임들을 목표 프레임을 기준으로 정렬한다. 마찬가지로 주요 아이디어는 정렬된 프레임을 합성할 때 특징 도메인에서 합성하는 것이다. 이는 픽셀 도메인에서 합성하거나 기하학적 변환을 이용할 때 보다 더 좋은 정렬 결과를 갖는다. 특히, 제안하는 정렬 네트워크는 두 갈래의 인코더와 컨볼루션 레이어들 그리고 디코더로 이루어져 있다. 인코더들은 두 입력 프레임으로부터 특징을 추출하고, 컨볼루션 레이어들이 이 특징들을 섞는다. 마지막으로 디코더에서 정렬된 프레임을 생성한다. 제안하는 네트워크는 인접한 프레임들을 잘 정렬하며, 비디오 고 해상화에 효과적으로 사용될 수 있다. 게다가 병합 네트워크를 추가하고 전체 네트워크들을 한 번에 학습함으로서, 최근에 제안된 여러 방법들 보다 더 좋은 성능을 갖는다. 고 명암비 영상법과 비디오 고 해상화에 더하여, 본 학위논문은 명암비와 해상도를 한 번에 향상시키는 딥 네트워크를 제안한다. 앞에서 제안된 두 네트워크들은 각각 명암비와 해상도를 향상시킨다. 하지만, 그들은 하나의 네트워크를 통해 한 번에 향상될 수 있다. 이 논문에서는 비디오 고해상화를 위해 제안한 네트워크와 같은 구조의 네트워크를 이용하며, 더 높은 명암비와 해상도를 갖는 최종 결과를 생성해낼 수 있다. 이 방법은 기존의 고 명암비 영상법과 비디오 고해상화를 위한 네트워크들을 조합하는 것 보다 정성적으로 그리고 정량적으로 더 좋은 결과를 만들어 낸다.1 Introduction 1 2 Related Work 7 2.1 High Dynamic Range Imaging 7 2.1.1 Rejecting Regions with Motions 7 2.1.2 Alignment Before Merging 8 2.1.3 Patch-based Reconstruction 9 2.1.4 Deep-learning-based Methods 9 2.1.5 Single-Image HDRI 10 2.2 Video Super-resolution 11 2.2.1 Deep Single Image Super-resolution 11 2.2.2 Deep Video Super-resolution 12 3 High Dynamic Range Imaging 13 3.1 Motivation 13 3.2 Proposed Method 14 3.2.1 Overall Pipeline 14 3.2.2 Alignment Network 15 3.2.3 Merging Network 19 3.2.4 Integrated HDR imaging network 20 3.3 Datasets 21 3.3.1 Kalantari Dataset and Ground Truth Aligned Images 21 3.3.2 Preprocessing 21 3.3.3 Patch Generation 22 3.4 Experimental Results 23 3.4.1 Evaluation Metrics 23 3.4.2 Ablation Studies 23 3.4.3 Comparisons with State-of-the-Art Methods 25 3.4.4 Application to the Case of More Numbers of Exposures 29 3.4.5 Pre-processing for other HDR imaging methods 32 4 Video Super-resolution 36 4.1 Motivation 36 4.2 Proposed Method 37 4.2.1 Overall Pipeline 37 4.2.2 Alignment Network 38 4.2.3 Reconstruction Network 40 4.2.4 Integrated VSR network 42 4.3 Experimental Results 42 4.3.1 Dataset 42 4.3.2 Ablation Study 42 4.3.3 Capability of DSBN for alignment 44 4.3.4 Comparisons with State-of-the-Art Methods 45 5 Joint HDR and SR 51 5.1 Proposed Method 51 5.1.1 Feature Blending Network 51 5.1.2 Joint HDR-SR Network 51 5.1.3 Existing VSR Network 52 5.1.4 Existing HDR Network 53 5.2 Experimental Results 53 6 Conclusion 58 Abstract (In Korean) 71Docto

Deep Burst Denoising

Noise is an inherent issue of low-light image capture, one which is exacerbated on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance. However, there are two downsides of long exposures: (a) bright regions can exceed the sensor range, and (b) camera and scene motion will result in blurred images. Another way of gathering more light is to capture multiple short (thus noisy) frames in a "burst" and intelligently integrate the content, thus avoiding the above downsides. In this paper, we use the burst-capture strategy and implement the intelligent integration via a recurrent fully convolutional deep neural net (CNN). We build our novel, multiframe architecture to be a simple addition to any single frame denoising model, and design to handle an arbitrary number of noisy input frames. We show that it achieves state of the art denoising results on our burst dataset, improving on the best published multi-frame techniques, such as VBM4D and FlexISP. Finally, we explore other applications of image enhancement by integrating content from multiple frames and demonstrate that our DNN architecture generalizes well to image super-resolution

Wideband Super-resolution Imaging in Radio Interferometry via Low Rankness and Joint Average Sparsity Models (HyperSARA)

We propose a new approach within the versatile framework of convex optimization to solve the radio-interferometric wideband imaging problem. Our approach, dubbed HyperSARA, solves a sequence of weighted nuclear norm and l21 minimization problems promoting low rankness and joint average sparsity of the wideband model cube. On the one hand, enforcing low rankness enhances the overall resolution of the reconstructed model cube by exploiting the correlation between the different channels. On the other hand, promoting joint average sparsity improves the overall sensitivity by rejecting artefacts present on the different channels. An adaptive Preconditioned Primal-Dual algorithm is adopted to solve the minimization problem. The algorithmic structure is highly scalable to large data sets and allows for imaging in the presence of unknown noise levels and calibration errors. We showcase the superior performance of the proposed approach, reflected in high-resolution images on simulations and real VLA observations with respect to single channel imaging and the CLEAN-based wideband imaging algorithm in the WSCLEAN software. Our MATLAB code is available online on GITHUB

Real Time Turbulent Video Perfecting by Image Stabilization and Super-Resolution

Image and video quality in Long Range Observation Systems (LOROS) suffer from atmospheric turbulence that causes small neighbourhoods in image frames to chaotically move in different directions and substantially hampers visual analysis of such image and video sequences. The paper presents a real-time algorithm for perfecting turbulence degraded videos by means of stabilization and resolution enhancement. The latter is achieved by exploiting the turbulent motion. The algorithm involves generation of a reference frame and estimation, for each incoming video frame, of a local image displacement map with respect to the reference frame; segmentation of the displacement map into two classes: stationary and moving objects and resolution enhancement of stationary objects, while preserving real motion. Experiments with synthetic and real-life sequences have shown that the enhanced videos, generated in real time, exhibit substantially better resolution and complete stabilization for stationary objects while retaining real motion.Comment: Submitted to The Seventh IASTED International Conference on Visualization, Imaging, and Image Processing (VIIP 2007) August, 2007 Palma de Mallorca, Spai

Exploiting flow dynamics for super-resolution in contrast-enhanced ultrasound

Ultrasound localization microscopy offers new radiation-free diagnostic tools for vascular imaging deep within the tissue. Sequential localization of echoes returned from inert microbubbles with low-concentration within the bloodstream reveal the vasculature with capillary resolution. Despite its high spatial resolution, low microbubble concentrations dictate the acquisition of tens of thousands of images, over the course of several seconds to tens of seconds, to produce a single super-resolved image. %since each echo is required to be well separated from adjacent microbubbles. Such long acquisition times and stringent constraints on microbubble concentration are undesirable in many clinical scenarios. To address these restrictions, sparsity-based approaches have recently been developed. These methods reduce the total acquisition time dramatically, while maintaining good spatial resolution in settings with considerable microbubble overlap. %Yet, non of the reported methods exploit the fact that microbubbles actually flow within the bloodstream. % to improve recovery. Here, we further improve sparsity-based super-resolution ultrasound imaging by exploiting the inherent flow of microbubbles and utilize their motion kinematics. While doing so, we also provide quantitative measurements of microbubble velocities. Our method relies on simultaneous tracking and super-localization of individual microbubbles in a frame-by-frame manner, and as such, may be suitable for real-time implementation. We demonstrate the effectiveness of the proposed approach on both simulations and {\it in-vivo} contrast enhanced human prostate scans, acquired with a clinically approved scanner.Comment: 11 pages, 9 figure