Uni-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images

Removing multiple degradations, such as haze, rain, and blur, from real-world images poses a challenging and illposed problem. Recently, unified models that can handle different degradations have been proposed and yield promising results. However, these approaches focus on synthetic images and experience a significant performance drop when applied to realworld images. In this paper, we introduce Uni-Removal, a twostage semi-supervised framework for addressing the removal of multiple degradations in real-world images using a unified model and parameters. In the knowledge transfer stage, Uni-Removal leverages a supervised multi-teacher and student architecture in the knowledge transfer stage to facilitate learning from pretrained teacher networks specialized in different degradation types. A multi-grained contrastive loss is introduced to enhance learning from feature and image spaces. In the domain adaptation stage, unsupervised fine-tuning is performed by incorporating an adversarial discriminator on real-world images. The integration of an extended multi-grained contrastive loss and generative adversarial loss enables the adaptation of the student network from synthetic to real-world domains. Extensive experiments on real-world degraded datasets demonstrate the effectiveness of our proposed method. We compare our Uni-Removal framework with state-of-the-art supervised and unsupervised methods, showcasing its promising results in real-world image dehazing, deraining, and deblurring simultaneously

Image Enhancement via Deep Spatial and Temporal Networks

Image enhancement is a classic problem in computer vision and has been studied for decades. It includes various subtasks such as super-resolution, image deblurring, rain removal and denoise. Among these tasks, image deblurring and rain removal have become increasingly active, as they play an important role in many areas such as autonomous driving, video surveillance and mobile applications. In addition, there exists connection between them. For example, blur and rain often degrade images simultaneously, and the performance of their removal rely on the spatial and temporal learning. To help generate sharp images and videos, in this thesis, we propose efficient algorithms based on deep neural networks for solving the problems of image deblurring and rain removal. In the first part of this thesis, we study the problem of image deblurring. Four deep learning based image deblurring methods are proposed. First, for single image deblurring, a new framework is presented which firstly learns how to transfer sharp images to realistic blurry images via a learning-to-blur Generative Adversarial Network (GAN) module, and then trains a learning-to-deblur GAN module to learn how to generate sharp images from blurry versions. In contrast to prior work which solely focuses on learning to deblur, the proposed method learns to realistically synthesize blurring effects using unpaired sharp and blurry images. Second, for video deblurring, spatio-temporal learning and adversarial training methods are used to recover sharp and realistic video frames from input blurry versions. 3D convolutional kernels on the basis of deep residual neural networks are employed to capture better spatio-temporal features, and train the proposed network with both the content loss and adversarial loss to drive the model to generate realistic frames. Third, the problem of extracting sharp image sequences from a single motion-blurred image is tackled. A detail-aware network is presented, which is a cascaded generator to handle the problems of ambiguity, subtle motion and loss of details. Finally, this thesis proposes a level-attention deblurring network, and constructs a new large-scale dataset including images with blur caused by various factors. We use this dataset to evaluate current deep deblurring methods and our proposed method. In the second part of this thesis, we study the problem of image deraining. Three deep learning based image deraining methods are proposed. First, for single image deraining, the problem of joint removal of raindrops and rain streaks is tackled. In contrast to most of prior works which solely focus on the raindrops or rain streaks removal, a dual attention-in-attention model is presented, which removes raindrops and rain streaks simultaneously. Second, for video deraining, a novel end-to-end framework is proposed to obtain the spatial representation, and temporal correlations based on ResNet-based and LSTM-based architectures, respectively. The proposed method can generate multiple deraining frames at a time, which outperforms the state-of-the-art methods in terms of quality and speed. Finally, for stereo image deraining, a deep stereo semantic-aware deraining network is proposed for the first time in computer vision. Different from the previous methods which only learn from pixel-level loss function or monocular information, the proposed network advances image deraining by leveraging semantic information and visual deviation between two views

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

Event-Based Fusion for Motion Deblurring with Cross-modal Attention

Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution, providing valid image degradation information within the exposure time. In this paper, we rethink the event-based image deblurring problem and unfold it into an end-to-end two-stage image restoration network. To effectively fuse event and image features, we design an event-image cross-modal attention module applied at multiple levels of our network, which allows to focus on relevant features from the event branch and filter out noise. We also introduce a novel symmetric cumulative event representation specifically for image deblurring as well as an event mask gated connection between the two stages of our network which helps avoid information loss. At the dataset level, to foster event-based motion deblurring and to facilitate evaluation on challenging real-world images, we introduce the Real Event Blur (REBlur) dataset, captured with an event camera in an illumination controlled optical laboratory. Our Event Fusion Network (EFNet) sets the new state of the art in motion deblurring, surpassing both the prior best-performing image-based method and all event-based methods with public implementations on the GoPro dataset (by up to 2.47dB) and on our REBlur dataset, even in extreme blurry conditions. The code and our REBlur dataset will be made publicly available

Event-Based Fusion for Motion Deblurring with Cross-modal Attention

Traditional frame-based cameras inevitably suffer from motion blur due to long exposure times. As a kind of bio-inspired camera, the event camera records the intensity changes in an asynchronous way with high temporal resolution, providing valid image degradation information within the exposure time. In this paper, we rethink the event-based image deblurring problem and unfold it into an end-to-end two-stage image restoration network. To effectively fuse event and image features, we design an event-image cross-modal attention module applied at multiple levels of our network, which allows to focus on relevant features from the event branch and filter out noise. We also introduce a novel symmetric cumulative event representation specifically for image deblurring as well as an event mask gated connection between the two stages of our network which helps avoid information loss. At the dataset level, to foster event-based motion deblurring and to facilitate evaluation on challenging real-world images, we introduce the Real Event Blur (REBlur) dataset, captured with an event camera in an illumination controlled optical laboratory. Our Event Fusion Network (EFNet) sets the new state of the art in motion deblurring, surpassing both the prior best-performing image-based method and all event-based methods with public implementations on the GoPro dataset (by up to 2.47dB) and on our REBlur dataset, even in extreme blurry conditions. The code and our REBlur dataset will be made publicly available