Learning Representations for Controllable Image Restoration

Deep Convolutional Neural Networks have sparked a renaissance in all the sub-fields of computer vision. Tremendous progress has been made in the area of image restoration. The research community has pushed the boundaries of image deblurring, super-resolution, and denoising. However, given a distorted image, most existing methods typically produce a single restored output. The tasks mentioned above are inherently ill-posed, leading to an infinite number of plausible solutions. This thesis focuses on designing image restoration techniques capable of producing multiple restored results and granting users more control over the restoration process. Towards this goal, we demonstrate how one could leverage the power of unsupervised representation learning. Image restoration is vital when applied to distorted images of human faces due to their social significance. Generative Adversarial Networks enable an unprecedented level of generated facial details combined with smooth latent space. We leverage the power of GANs towards the goal of learning controllable neural face representations. We demonstrate how to learn an inverse mapping from image space to these latent representations, tuning these representations towards a specific task, and finally manipulating latent codes in these spaces. For example, we show how GANs and their inverse mappings enable the restoration and editing of faces in the context of extreme face super-resolution and the generation of novel view sharp videos from a single motion-blurred image of a face. This thesis also addresses more general blind super-resolution, denoising, and scratch removal problems, where blur kernels and noise levels are unknown. We resort to contrastive representation learning and first learn the latent space of degradations. We demonstrate that the learned representation allows inference of ground-truth degradation parameters and can guide the restoration process. Moreover, it enables control over the amount of deblurring and denoising in the restoration via manipulation of latent degradation features

Continuous Facial Motion Deblurring

We introduce a novel framework for continuous facial motion deblurring that restores the continuous sharp moment latent in a single motion-blurred face image via a moment control factor. Although a motion-blurred image is the accumulated signal of continuous sharp moments during the exposure time, most existing single image deblurring approaches aim to restore a fixed number of frames using multiple networks and training stages. To address this problem, we propose a continuous facial motion deblurring network based on GAN (CFMD-GAN), which is a novel framework for restoring the continuous moment latent in a single motion-blurred face image with a single network and a single training stage. To stabilize the network training, we train the generator to restore continuous moments in the order determined by our facial motion-based reordering process (FMR) utilizing domain-specific knowledge of the face. Moreover, we propose an auxiliary regressor that helps our generator produce more accurate images by estimating continuous sharp moments. Furthermore, we introduce a control-adaptive (ContAda) block that performs spatially deformable convolution and channel-wise attention as a function of the control factor. Extensive experiments on the 300VW datasets demonstrate that the proposed framework generates a various number of continuous output frames by varying the moment control factor. Compared with the recent single-to-single image deblurring networks trained with the same 300VW training set, the proposed method show the superior performance in restoring the central sharp frame in terms of perceptual metrics, including LPIPS, FID and Arcface identity distance. The proposed method outperforms the existing single-to-video deblurring method for both qualitative and quantitative comparisons

Motion deblurring of faces

Face analysis is a core part of computer vision, in which remarkable progress has been observed in the past decades. Current methods achieve recognition and tracking with invariance to fundamental modes of variation such as illumination, 3D pose, expressions. Notwithstanding, a much less standing mode of variation is motion deblurring, which however presents substantial challenges in face analysis. Recent approaches either make oversimplifying assumptions, e.g. in cases of joint optimization with other tasks, or fail to preserve the highly structured shape/identity information. Therefore, we propose a data-driven method that encourages identity preservation. The proposed model includes two parallel streams (sub-networks): the first deblurs the image, the second implicitly extracts and projects the identity of both the sharp and the blurred image in similar subspaces. We devise a method for creating realistic motion blur by averaging a variable number of frames to train our model. The averaged images originate from a 2MF2 dataset with 10 million facial frames, which we introduce for the task. Considering deblurring as an intermediate step, we utilize the deblurred outputs to conduct a thorough experimentation on high-level face analysis tasks, i.e. landmark localization and face verification. The experimental evaluation demonstrates the superiority of our method

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

FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring

Blind image deblurring (BID) remains a challenging and significant task. Benefiting from the strong fitting ability of deep learning, paired data-driven supervised BID method has obtained great progress. However, paired data are usually synthesized by hand, and the realistic blurs are more complex than synthetic ones, which makes the supervised methods inept at modeling realistic blurs and hinders their real-world applications. As such, unsupervised deep BID method without paired data offers certain advantages, but current methods still suffer from some drawbacks, e.g., bulky model size, long inference time, and strict image resolution and domain requirements. In this paper, we propose a lightweight and real-time unsupervised BID baseline, termed Frequency-domain Contrastive Loss Constrained Lightweight CycleGAN (shortly, FCL-GAN), with attractive properties, i.e., no image domain limitation, no image resolution limitation, 25x lighter than SOTA, and 5x faster than SOTA. To guarantee the lightweight property and performance superiority, two new collaboration units called lightweight domain conversion unit(LDCU) and parameter-free frequency-domain contrastive unit(PFCU) are designed. LDCU mainly implements inter-domain conversion in lightweight manner. PFCU further explores the similarity measure, external difference and internal connection between the blurred domain and sharp domain images in frequency domain, without involving extra parameters. Extensive experiments on several image datasets demonstrate the effectiveness of our FCL-GAN in terms of performance, model size and reference time