Masked Image Training for Generalizable Deep Image Denoising
Shanghai AI Lab（上海人工智能实验室）；The University of Sydney（悉尼大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；Harvard University（哈佛大学）；The Hong Kong University of Science and Technology（香港科技大学）
When capturing and storing images, devices inevitably introduce noise. Reducing this noise is a critical task called image denoising. Deep learning has become the de facto method for image denoising, especially with the emergence of Transformer-based models that have achieved notable state-of-the-art results on various image tasks. However, deep learning-based methods often suffer from a lack of generalization ability. For example, deep models trained on Gaussian noise may perform poorly when tested on other noise distributions. To address this issue, we present a novel approach to enhance the generalization performance of denoising networks, known as masked training. Our method involves masking random pixels of the input image and reconstructing the missing information during training. We also mask out the features in the self-attention layers to avoid the impact of training-testing inconsistency. Our approach exhibits better generalization ability than other deep learning models and is directly applicable to real-world scenarios. Additionally, our interpretability analysis demonstrates the superiority of our method.
IDF: Iterative Dynamic Filtering Networks for Generalizable Image Denoising
Department of Computer Science, Hanyang University（翰阳大学计算机科学系）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）
U-TTT: Towards Generalizable PET Image Denoising via Test-Time Training
School of Biological Science and Medical Engineering, Beihang University（北京航空航天大学生物科学与医学工程学院）；Department of Biomedical Engineering, Tsinghua University（清华大学生物医学工程系）；School of Aerospace Engineering, Tsinghua University（清华大学航天航空学院）；ByteDance Inc.（字节跳动有限公司）
Nanyang Technological University（南洋理工大学）；University of Science and Technology of China（中国科学技术大学）；Nankai University（南开大学）
Image and video restoration has achieved a remarkable leap with the advent of deep learning. The success of deep learning paradigm lies in three key components: data, model, and loss. Currently, many efforts have been devoted to the first two while seldom study focuses on loss function. With the question ``are the de facto optimization functions e.g., $L_1$, $L_2$, and perceptual losses optimal?'', we explore the potential of loss and raise our belief ``learned loss function empowers the learning capability of neural networks for image and video restoration''. Concretely, we stand on the shoul…
Korea Advanced Institute of Science and Technology（韩国科学技术院）
In recent years, a ton of research has been conducted on real image denoising tasks. However, the efforts are more focused on improving real image denoising through creating a better network architecture. We explore a different direction where we propose to improve real image denoising performance through a better learning strategy that can enable test-time adaptation on the multi-task network. The learning strategy is two stages where the first stage pre-train the network using meta-auxiliary learning to get better meta-initialization. Meanwhile, we use meta-learning for fine-tuning (meta-tra…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；School of Future Technology, University of Chinese Academy of Sciences（中国科学院大学未来技术学院）
Self-supervised blind denoising for Poisson-Gaussian noise remains a challenging task. Pseudo-supervised pairs constructed from single noisy images re-corrupt the signal and degrade the performance. The visible blindspots solve the information loss in masked inputs. However, without explicitly noise sensing, mean square error as an objective function cannot adjust denoising intensities for dynamic noise levels, leading to noticeable residual noise. In this paper, we propose Blind2Sound, a simple yet effective approach to overcome residual noise in denoised images. The proposed adaptive re-visi…
SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions
Institute of Computer Science, University of Tartu（塔尔图大学计算机科学学院）；CAIRO, Technical University of Applied Sciences Würzburg-Schweinfurt（维尔茨堡-施韦因富特应用技术大学CAIRO机构）
Nanjing University of Posts and Telecommunications（南京邮电大学）；Soochow University（苏州大学）；Kyushu Institute of Technology（九州工业大学）；Nanjing University of Science and Technology（南京理工大学）
With the growing importance of preventing the COVID-19 virus, face images obtained in most video surveillance scenarios are low resolution with mask simultaneously. However, most of the previous face super-resolution solutions can not handle both tasks in one model. In this work, we treat the mask occlusion as image noise and construct a joint and collaborative learning network, called JDSR-GAN, for the masked face super-resolution task. Given a low-quality face image with the mask as input, the role of the generator composed of a denoising module and super-resolution module is to acquire a hi…
Recently, significant progress has been made on image denoising with strong supervision from large-scale datasets. However, obtaining well-aligned noisy-clean training image pairs for each specific scenario is complicated and costly in practice. Consequently, applying a conventional supervised denoising network on in-the-wild noisy inputs is not straightforward. Although several studies have challenged this problem without strong supervision, they rely on less practical assumptions and cannot be applied to practical situations directly. To address the aforementioned challenges, we propose a no…
ARC Lab（ARC实验室）；Peking University（北京大学）；Huazhong University of Science and Technology（华中科技大学）；Foundation Technology Center（基础技术中心）；Tsinghua University（清华大学）；Tencent PCG（腾讯平台与内容事业群）
Since the development of self-supervised visual representation learning from contrastive learning to masked image modeling (MIM), there is no significant difference in essence, that is, how to design proper pretext tasks for vision dictionary look-up. MIM recently dominates this line of research with state-of-the-art performance on vision Transformers (ViTs), where the core is to enhance the patch-level visual context capturing of the network via denoising auto-encoding mechanism. Rather than tailoring image tokenizers with extra training stages as in previous works, we unleash the great poten…
FoundDiff: Foundational Diffusion Model for Generalizable Low-Dose CT Denoising
Institute of Science and Technology for Brain-inspired Intelligence and MOE Frontiers Center for Brain Science, Fudan University（脑启发智能科学技术研究所和脑科学前沿中心，复旦大学）；Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University（智能信息处理上海市重点实验室，复旦大学计算机科学学院）；School of Cyber Science and Engineering, Sichuan University（四川大学网络科学与工程学院）；School of Biomedical Engineering, Shanghai Jiao Tong University（上海交通大学生物医学工程学院）
Low-dose computed tomography (LDCT) reduces the X-ray radiation but compromises image quality with more noises and artifacts. A plethora of transformer models have been developed recently to improve LDCT image quality. However, the success of a transformer model relies on a large amount of paired noisy and clean data, which is often unavailable in clinical applications. In computer vision and natural language processing fields, masked autoencoders (MAE) have been proposed as an effective label-free self-pretraining method for transformers, due to its excellent feature representation ability. H…
Recent Self-Supervised Learning (SSL) methods are able to learn feature representations that are invariant to different data augmentations, which can then be transferred to downstream tasks of interest. However, different downstream tasks require different invariances for their best performance, so the optimal choice of augmentations for SSL depends on the target task. In this paper, we aim to learn self-supervised features that generalize well across a variety of downstream tasks (e.g., object classification, detection and instance segmentation) without knowing any task information beforehand…
Peking University（北京大学）；Microsoft Research Asia（微软亚洲研究院）
In this paper, we propose a new self-supervised method, which is called Denoising Masked AutoEncoders (DMAE), for learning certified robust classifiers of images. In DMAE, we corrupt each image by adding Gaussian noises to each pixel value and randomly masking several patches. A Transformer-based encoder-decoder model is then trained to reconstruct the original image from the corrupted one. In this learning paradigm, the encoder will learn to capture relevant semantics for the downstream tasks, which is also robust to Gaussian additive noises. We show that the pre-trained encoder can naturally…
Universidad Politécnica de Madrid（马德里理工大学）；Centre national de la recherche scientifique (CNRS)（法国国家科学研究中心）；Interdisciplinary Institute for Neuroscience (IINS)（跨学科神经科学研究所）；University of Bordeaux（波尔多大学）；Mechanobiology Institute（机械生物学研究所）；National University of Singapore（新加坡国立大学）
Microscopy images often suffer from high levels of noise, which can hinder further analysis and interpretation. Content-aware image restoration (CARE) methods have been proposed to address this issue, but they often require large amounts of training data and suffer from over-fitting. To overcome these challenges, we propose a novel framework for few-shot microscopy image denoising. Our approach combines a generative adversarial network (GAN) trained via contrastive learning (CL) with two structure preserving loss terms (Structural Similarity Index and Total Variation loss) to further improve t…
Sun Yat-sen University（中山大学）；The Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education（机器智能与先进计算教育部重点实验室（中山大学））；JD AI Research（京东人工智能研究院）
BERT-type structure has led to the revolution of vision-language pre-training and the achievement of state-of-the-art results on numerous vision-language downstream tasks. Existing solutions dominantly capitalize on the multi-modal inputs with mask tokens to trigger mask-based proxy pre-training tasks (e.g., masked language modeling and masked object/frame prediction). In this work, we argue that such masked inputs would inevitably introduce noise for cross-modal matching proxy task, and thus leave the inherent vision-language association under-explored. As an alternative, we derive a particul…
Building scalable models to learn from diverse, multimodal data remains an open challenge. For vision-language data, the dominant approaches are based on contrastive learning objectives that train a separate encoder for each modality. While effective, contrastive learning approaches introduce sampling bias depending on the data augmentations used, which can degrade performance on downstream tasks. Moreover, these methods are limited to paired image-text data, and cannot leverage widely-available unpaired data. In this paper, we investigate whether a large multimodal model trained purely via ma…
Unsupervised Region-Based Image Editing of Denoising Diffusion Models
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Pengcheng Laboratory（鹏城实验室）；University of Trento（特伦托大学）；Lanzhou Jiaotong University（兰州交通大学）
UrFound: Towards Universal Retinal Foundation Models via Knowledge-Guided Masked Modeling
Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR)（高性能计算研究所（A*STAR））；Singapore Eye Research Institute, Singapore National Eye Centre（新加坡眼科研究所，新加坡国家眼科中心）
Scene Graph Disentanglement and Composition for Generalizable Complex Image Generation
Shanghai Jiao Tong University（上海交通大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东方理工大学宁波数字孪生研究院）；University of Science and Technology of China（中国科学技术大学）；The University of Hong Kong（香港大学）
We propose DoPAMINE, a new neural network based multiplicative noise despeckling algorithm. Our algorithm is inspired by Neural AIDE (N-AIDE), which is a recently proposed neural adaptive image denoiser. While the original N-AIDE was designed for the additive noise case, we show that the same framework, i.e., adaptively learning a network for pixel-wise affine denoisers by minimizing an unbiased estimate of MSE, can be applied to the multiplicative noise case as well. Moreover, we derive a double-sided masked CNN architecture which can control the variance of the activation values in each laye…
DRACO: A Denoising-Reconstruction Autoencoder for Cryo-EM
School of Information Science and Technology, ShanghaiTech University（上海科技大学信息科学与技术学院）；Cellverse Co, Ltd.（Cellverse公司）；iHuman Institute, ShanghaiTech University（上海科技大学iHuman研究所）
