Image restoration tasks demand a complex balance between spatial details and high-level contextualized information while recovering images. In this paper, we propose a novel synergistic design that can optimally balance these competing goals. Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, our model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features. A key ingredient in such a multi-stage architecture is the information exchange between different stages. To this end, we propose a two-faceted approach where the information is not only exchanged sequentially from early to late stages, but lateral connections between feature processing blocks also exist to avoid any loss of information. The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets across a range of tasks including image deraining, deblurring, and denoising. The source code and pre-trained models are available at https://github.com/swz30/MPRNet.

/pdf/multi-stage-progressive-image-restoration-3wfddniu8m.pdf

Multi-Stage Progressive Image Restoration

A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic

The current superresolution (SR) methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, e.g., remote sensing satellite imaging. In this paper, we propose a generative adversarial network (GAN)-based edge-enhancement network (EEGAN) for robust satellite image SR reconstruction along with the adversarial learning strategy that is insensitive to noise. In particular, EEGAN consists of two main subnetworks: an ultradense subnetwork (UDSN) and an edge-enhancement subnetwork (EESN). In UDSN, a group of 2-D dense blocks is assembled for feature extraction and to obtain an intermediate high-resolution result that looks sharp but is eroded with artifacts and noises as previous GAN-based methods do. Then, EESN is constructed to extract and enhance the image contours by purifying the noise-contaminated components with mask processing. The recovered intermediate image and enhanced edges can be combined to generate the result that enjoys high credibility and clear contents. Extensive experiments on Kaggle Open Source Data set , Jilin-1 video satellite images, and Digitalglobe show superior reconstruction performance compared to the state-of-the-art SR approaches.

Edge-Enhanced GAN for Remote Sensing Image Superresolution

In order to effectively prevent the spread of COVID-19 virus, almost everyone wears a mask during coronavirus epidemic. This almost makes conventional facial recognition technology ineffective in many cases, such as community access control, face access control, facial attendance, facial security checks at train stations, etc. Therefore, it is very urgent to improve the recognition performance of the existing face recognition technology on the masked faces. Most current advanced face recognition approaches are designed based on deep learning, which depend on a large number of face samples. However, at present, there are no publicly available masked face recognition datasets. To this end, this work proposes three types of masked face datasets, including Masked Face Detection Dataset (MFDD), Real-world Masked Face Recognition Dataset (RMFRD) and Simulated Masked Face Recognition Dataset (SMFRD). Among them, to the best of our knowledge, RMFRD is currently theworld's largest real-world masked face dataset. These datasets are freely available to industry and academia, based on which various applications on masked faces can be developed. The multi-granularity masked face recognition model we developed achieves 95% accuracy, exceeding the results reported by the industry. Our datasets are available at: this https URL.

Masked Face Recognition Dataset and Application

In this paper, we explore the role of Instance Normalization in low-level vision tasks. Specifically, we present a novel block: Half Instance Normalization Block (HIN Block), to boost the performance of image restoration networks. Based on HIN Block, we design a simple and powerful multi-stage network named HINet, which consists of two subnetworks. With the help of HIN Block, HINet surpasses the state-of-the-art (SOTA) on various image restoration tasks. For image denoising, we exceed it 0.11dB and 0.28 dB in PSNR on SIDD dataset, with only 7.5% and 30% of its multiplier-accumulator operations (MACs), 6.8× and 2.9× speedup respectively. For image deblurring, we get comparable performance with 22.5% of its MACs and 3.3× speedup on REDS and GoPro datasets. For image deraining, we exceed it by 0.3 dB in PSNR on the average result of multiple datasets with 1.4× speedup. With HINet, we won the 1st place on the NTIRE 2021 Image Deblurring Challenge - Track2. JPEG Artifacts, with a PSNR of 29.70.

/pdf/hinet-half-instance-normalization-network-for-image-34473ablug.pdf

HINet: Half Instance Normalization Network for Image Restoration

Existing space-time video super-resolution (ST-VSR) methods fail to achieve high-quality reconstruction since they fail to fully explore the spatial-temporal correlations, long-range components in particular. Although the recurrent structure for ST-VSR adopts bidirectional propagation to aggregate information from the entire video, collecting the temporal information between the past and future via one-stage representations inevitably loses the long-range relations. To alleviate the limitation, this paper proposes an immediate storeand-fetch network to promote long-range correlation learning, where the stored information from the past and future can be refetched to help the representation of the current frame. Specifically, the proposed network consists of two modules: a backward recurrent module (BRM) and a forward recurrent module (FRM). The former first performs backward inference from future to past, while storing future super-resolution (SR) information for each frame. Following that, the latter performs forward inference from past to future to super-resolve all frames, while storing past SR information for each frame. Since FRM inherits SR information from BRM, therefore, spatial and temporal information from the entire video sequence is immediately stored and fetched, which allows drastic improvement for ST-VSR. Extensive experiments both on ST-VSR and space video super-resolution (S-VSR) as well as time video super-resolution (T-VSR) have demonstrated the effectiveness of our proposed method over other state-of-the-art methods on public datasets. Code is available https://github.com/hhhhhumengshun/SFI-STVR

https://ojs.aaai.org/index.php/AAAI/article/download/25165/24937

Store and Fetch Immediately: Everything Is All You Need for Space-Time Video Super-resolution

PM2.5 in the atmosphere causes severe air pollution and dramatically affects the normal production and lives of residents. The real-time monitoring of PM2.5 concentrations has important practical significance for the construction of ecological civilization. The mainstream PM2.5 concentration prediction algorithms based on electrochemical sensors have some disadvantages, such as high economic cost, high labor cost, time delay, and more. To this end, we propose a simple and effective PM2.5 concentration prediction algorithm based on image perception. Specifically, the proposed method develops a natural scene statistical prior to estimating the saturation loss caused by the ’haze’ formed by PM2.5. After extracting the prior features, this paper uses the feedforward neural network to achieve the mapping function from the proposed prior features to the PM2.5 concentration values. Experiments constructed on the public Air Quality Image Dataset (AQID) show the superiority of our proposed PM2.5 concentration measurement method compared to state-of-the-art related PM2.5 concentration monitoring methods.

/pdf/pm2-5-concentration-measurement-based-on-image-perception-1quc5kje.pdf

PM2.5 Concentration Measurement Based on Image Perception

Deeper and wider convolutional neural networks (CNN) hava been widely applied to the single image super-resolution (SR) task for its appealing performance. However, enormous parametric memory footprint hinders its real-time application on mobile devices, especially in the energy-sensitive environment. In this work, we take both the reconstruction performance and efficiency into consideration and propose a lightweight progressive residual clique network (PRCN) for image SR. PRCN is built on the two-stage residual channel separation block (RCSB) and long-skip connections. First, we divide the input into four channel groups to differently learn texture details, immediately followed by a primary fusion to establish cross-channel correspondence in the first stage. Then we perform a further fusion on the outputs of the first stage to constitute a clique for the refinement in the second stage. Meanwhile, we employ SENet to improve the outputs of the second stage with the separate features of the first stage. This design not only enforces the correlation across channels, but also allows fewer densely connected blocks. Experimental results on public datasets show that PRCN outperforms state-of-the-art methods in terms of performance and complexity.

Lightweight Progressive Residual Clique Network for Image Super-Resolution

High-resolution (HR) hyperspectral face image plays an important role in face related computer vision tasks under uncontrolled conditions, such as low-light environment and spoofing attacks. However, the dense spectral bands of hyperspectral face images come at the cost of limited amount of photons reached a narrow spectral window on average, which greatly reduces the spatial resolution of hyperspectral face images. In this paper, we investigate how to adapt the deep learning techniques to hyperspectral face image super-resolution (HFSR), especially when the training samples are very limited. Benefiting from the amount of spectral bands, in which each band can be seen as an image, we present a spectral splitting and aggregation network (SSANet) for HFSR with limited training samples. In the shallow layers, we split the hyperspectral image into different spectral groups and take each of them as an individual training sample (in the sense that each group will be fed into the same network). Then, we gradually aggregate the neighbor bands at the deeper layers to exploit the spectral correlations. By this spectral splitting and aggregation strategy (SSAS), we can divide the original hyperspectral image into multiple samples to support the efficient training of the network and effectively exploit the spectral correlations among spectrum. To cope with the challenge of small training sample size (S3) problem, we propose to expand the training samples by a self-representation model and symmetry-induced augmentation. Experiments show that the introduced SSANet can well model the joint correlations of spatial and spectral information. By expanding the training samples, our proposed method can effectively alleviate the S3 problem. The comparison results demonstrate that our proposed method can outperform the state-of-the-arts.

Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution

During the COVID-19 coronavirus epidemic, wearing masks has become increasingly popular. Traditional occlusion face recognition algorithms are almost ineffective for such heavy mask occlusion. Therefore, it is urgent to improve the recognition performance of the existing face recognition technology on masked faces. Due to the limited visible feature points of the masked face image relative to the normal face image, we have to exploit the identification potential of eyebrow (referring to eyes and brows) features. This article proposes a local eyebrow feature attention network for masked face recognition, which consists of feature extraction, eyebrow region pooling, and feature fusion. To highlight the eyebrow region, we first use the eyebrow region pooling to separate the local features of eyebrows from the learned overall facial features. We then make full use of the symmetry of left and right eyebrows to emphasize their discriminant ability, due to the inadequate fine information of the low-resolution eyebrows. In particular, in view of the symmetrical similarity between eyebrow pairs and the subordinate relationship between facial components and the whole, we propose a feature fusion model based on graph convolutional network (GCN) to learn the feature association structure of eye features, brow features, and global facial features. We construct the benchmark datasets for masked face recognition to validate our approach, including real-world masked face recognition dataset (RMFRD) and synthetic masked face recognition dataset (SMFRD). Extensive experimental results on both public datasets and our built masked face datasets show that our approach significantly outperforms the state-of-the-arts.

Kui Jiang

Papers

Store and Fetch Immediately: Everything Is All You Need for Space-Time Video Super-resolution

PM2.5 Concentration Measurement Based on Image Perception

Lightweight Progressive Residual Clique Network for Image Super-Resolution

Spectral Splitting and Aggregation Network for Hyperspectral Face Super-Resolution

Local Eyebrow Feature Attention Network for Masked Face Recognition