Enhancing Video Deblurring using Efficient Fourier Aggregation

Video Deblurring is a process of removing blur from all the video frames and achieving the required level of smoothness. Numerous recent approaches attempt to remove image blur due to camera shake,either with one or multiple input images, by explicitly solving an inverse and inherently ill-posed deconvolution problem.An efficient video deblurring system to handle the blurs due to shaky camera and complex motion blurs due to moving objects has been proposed.The proposed algorithm is strikingly simple: it performs a weighted average in the Fourier domain, with weights depending on the Fourier spectrum magnitude. The method can be seen as a generalization of the align and average procedure, with a weighted average, motivated by hand-shake physiology and theoretically supported, taking place in the Fourier domain. The method�s rationale is that camera shake has a random nature, and therefore, each image in the burst is generally blurred differently.The proposed system has effectively deblurred the video and results showed that the reconstructed video is sharper and less noisy than the original ones.The proposed Fourier Burst Accumulation algorithm produced similar or better results than the state-of-the-art multi-image deconvolution while being significantly faster and with lower memory footprint.The method is robust to moving objects as it acquired the consistent registration scheme

Learning to Extract a Video Sequence from a Single Motion-Blurred Image

We present a method to extract a video sequence from a single motion-blurred image. Motion-blurred images are the result of an averaging process, where instant frames are accumulated over time during the exposure of the sensor. Unfortunately, reversing this process is nontrivial. Firstly, averaging destroys the temporal ordering of the frames. Secondly, the recovery of a single frame is a blind deconvolution task, which is highly ill-posed. We present a deep learning scheme that gradually reconstructs a temporal ordering by sequentially extracting pairs of frames. Our main contribution is to introduce loss functions invariant to the temporal order. This lets a neural network choose during training what frame to output among the possible combinations. We also address the ill-posedness of deblurring by designing a network with a large receptive field and implemented via resampling to achieve a higher computational efficiency. Our proposed method can successfully retrieve sharp image sequences from a single motion blurred image and can generalize well on synthetic and real datasets captured with different cameras

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