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Toward Interactive Modulation for Photo-Realistic Image Restoration

Toward Interactive Modulation for Photo-Realistic Image Rest...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Cai, Haoming He, Jingwen Qiao, Yu Dong, Chao Chinese Acad Sci Shenzhen Inst Adv Technol Key Lab Human Machine Intelligence Synergy Syst Shenzhen Peoples R China Shanghai AI Lab Shanghai Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc SIAT Branch Shenzhen Peoples R China

ISBN: (纸本)9781665448994

Modulating image restoration level aims to generate a restored image by altering a factor that represents the restoration strength. Previous works mainly focused on optimizing the mean squared reconstruction error, which brings high reconstruction accuracy but lacks finer texture details. This paper presents a Controllable Unet Generative Adversarial Network (CUGAN) to generate high frequency textures in the modulation tasks. CUGAN consists of two modules - base networks and condition networks. The base networks comprise a generator and a discriminator. In the generator, we realize the interactive control of restoration levels by tuning the weights of different features from different scales in the Unet architecture. Moreover, we adaptively modulate the intermediate features in the discriminator according to the severity of degradations. The condition networks accept the condition vector (encoded degradation information) as input, then generate modulation parameters for both the generator and the discriminator. During testing, users can control the output effects by tweaking the condition vector. We also provide a smooth transition between GAN and MSE effects by a simple transition method. Extensive experiments demonstrate that the proposed CUGAN achieves excellent performance on image restoration modulation tasks.

关键词： Degradation Modulation Generative adversarial networks Generators Image restoration pattern recognition Task analysis

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Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information

Boosting the Performance of Video Compression Artifact Reduc...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Xu, Yi Zhao, Minyi Liu, Jing Zhang, Xinjian Gao, Longwen Zhou, Shuigeng Sun, Huyang Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China Bilibili Shanghai Peoples R China

ISBN: (纸本)9781665448994

Many deep learning based video compression artifact removal algorithms have been proposed to recover high-quality videos from low-quality compressed videos. Recently, methods were proposed to mine spatiotemporal information via utilizing multiple neighboring frames as reference frames. However, these post-processing methods take advantage of adjacent frames directly, but neglect the information of the video itself which can be exploited. In this paper, we propose an effective reference frame proposal strategy to boost the performance of the existing multi-frame approaches. Besides, we introduce a loss based on fast Fourier transformation (FFT) to further improve the effectiveness of restoration. Experimental results show that our method achieves better fidelity and perceptual performance on MFQE 2.0 dataset than the state-of-the-art methods. And our method won Track I and Track 2, and was ranked the 2nd in Track 3 of NTIRE 2021 Quality enhancement of heavily compressed videos Challenge.

关键词： Deep learning computer vision Frequency-domain analysis conferences Video compression Boosting Spatiotemporal phenomena

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Detection of Motion Vector-Based Stegomalware in Video Files 1

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International conference on computer vision and Machine Intelligence, CVMI 2022

作者： Nair, Sandra V. S. Arun Raj Kumar, P. Department of Computer Science and Engineering National Institute of Technology Calicut Kozhikode India

ISBN: (数字)9789811978678

ISBN: (纸本)9789811978661

Cybercriminals are increasingly using steganography to launch attacks on devices. The cyberattack is more threatening as steganography hides the embedded malware, if any, making it harder to detect by various anti-virus tools. Such malware is called stegomalware. Since video files are larger and have a complex structure, they also have a high capacity for hiding malware. Motion vector (MV)-based steganography techniques do not cause much distortion in video files. Therefore, it remains to be one of the leading video steganography techniques. This paper deals with a lightweight solution for MV-based stegomalware detection in video files. Our model is compatible with state-of-the-art video coding standards having variable macroblock sizes and different motion vector resolutions. The proposed method obtained an accuracy of 95.8% on testing H.264 videos with various embedding rates. The 81-D spatial and temporal features result in the high performance of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Video signal processing

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Kullback–Leibler Distance-Based Fuzzy K-Plane Clustering Approach for Noisy Human Brain MRI Image Segmentation

Kullback–Leibler Distance-Based Fuzzy K-Plane Clustering Ap...

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International conference on computer vision and Machine Intelligence, CVMI 2022

作者： Kumar, Puneet Agrawal, R.K. Kumar, Dhirendra School of Computer & Systems Sciences Jawaharlal Nehru University Delhi India Department of Applied Mathematics Delhi Technological University Delhi India

ISBN: (纸本)9789811978661

MR images are complex as the data distribution of tissues in MR images is non-spherical and overlapping in nature. The fuzzy k-plane clustering method (FkPC) is the most suitable soft clustering method to cluster non-spherical shape data and solve the overlapping problem of MR tissues. Generally, MR images are corrupted with imaging artifacts such as noise and intensity inhomogeneity. The FkPC method performs poor in the presence of noise. In this work, a new objective function for handling noise in the plane-based clustering approach is discussed with application to the MR image segmentation problem. We have utilised Kullback–Leibler (KL) distance measure to dampen the effect of noise in the k-plane clustering approach for the image segmentation problem. The proposed method is termed as KL distance-based fuzzy k-plane clustering (KLFkPC) method. Three publicly available MRI image datasets were utilised in the study to assess the efficiency of the proposed KLFkPC approach. The results are evaluated using a variety of performance metrics that demonstrate the efficacy of the proposed method over the ten existing related methods. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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***: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery

***: Dataset for Automatic Mapping of Buildings, Woodlands, ...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Boguszewski, Adrian Batorski, Dominik Ziemba-Jankowska, Natalia Dziedzic, Tomasz Zambrzycka, Anna Linux Polska Warsaw Poland Univ Warsaw Interdisciplinary Ctr Math & Computat Modelling Warsaw Poland Agcy Restructuring & Modernisat Agr Warsaw Poland

ISBN: (纸本)9781665448994

Monitoring of land cover and land use is crucial in natural resources management. Automatic visual mapping can carry enormous economic value for agriculture, forestry, or public administration. Satellite or aerial images combined with computer vision and deep learning enable precise assessment and can significantly speed up change detection. Aerial imagery usually provides images with much higher pixel resolution than satellite data allowing more detailed mapping. However, there is still a lack of aerial datasets made for the segmentation, covering rural areas with a resolution of tens centimeters per pixel, manual fine labels, and highly publicly important environmental instances like buildings, woods, water, or roads. Here we introduce *** (Land Cover from Aerial Imagery) dataset for semantic segmentation. We collected images of 216.27 km(2) rural areas across Poland, a country in Central Europe, 39.51 km(2) with resolution 50 cm per pixel and 176.76 km(2) with resolution 25 cm per pixel and manually fine annotated four following classes of objects: buildings, woodlands, water, and roads. Additionally, we report simple benchmark results, achieving 85.56% of mean intersection over union on the test set. It proves that the automatic mapping of land cover is possible with a relatively small, cost-efficient, RGB-only dataset. The dataset is publicly available at https://***/

关键词： Deep learning Image segmentation computer vision Image resolution Satellites Roads Buildings

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Face Parsing from RGB and Depth Using Cross-Domain Mutual Learning

Face Parsing from RGB and Depth Using Cross-Domain Mutual Le...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Lee, Jihyun Bhattarai, Binod Kim, Tae-Kyun Korea Adv Inst Sci & Technol Seoul South Korea Imperial Coll London London England

ISBN: (纸本)9781665448994

Existing methods of face parsing have proven effective at classifying each pixel of an RGB image into different facial components. However, there is a lack of face parsing research that utilizes depth domain. To the best of our knowledge, we present the first study to exploit 2.5D data for face parsing. We introduce a novel framework to jointly learn (1) RGB face parsing, (2) depth face parsing and (3) RGB-to-depth domain translation, which can be effective even when only a small amount of annotated depth data is available for training. To this end, we also create the first RGB-D face parsing benchmarks based on CelebAMask-HQ, LaPa and Helen by utilizing an off-the-shelf 3D head reconstruction model. Overall, our approach makes two main contributions. First, our method leverages mutual learning between RGB and depth face parsing, which enables bidirectional knowledge distillation between the two data domains. Second, our method utilizes end-to-end learning of RGB-to-depth domain translation and depth face parsing, which can help overcome the scarcity of annotated depth data. We perform extensive experiments to validate the effectiveness of our method, in which we achieve state-of-the-art results in RGB face parsing. As far as we know, we also report the first results on face parsing from depth data. All experiments are conducted on our new RGB-D face parsing datasets, which are publicly available at https://***/jyunlee/CelebAMaskHQ-D_LaPa-D_Helen-D.

关键词： Training Solid modeling computer vision Three-dimensional displays Head Face recognition conferences

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MIPI 2024 Challenge on Nighttime Flare Removal: Methods and Results

MIPI 2024 Challenge on Nighttime Flare Removal: Methods and ...

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ieee computer society conference on computer vision and pattern recognition Workshops (cvprW)

作者： Yuekun Dai Dafeng Zhang Xiaoming Li Zongsheng Yue Chongyi Li Shangchen Zhou Ruicheng Feng Peiqing Yang Zhezhu Jin Guanqun Liu Chen Change Loy S-Lab Nanyang Technological University Samsung Research China Nankai University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The increasing demand for computational photography and imaging on mobile platforms has led to the widespread development and integration of advanced image sensors with novel algorithms in camera systems. However, the scarcity of high-quality data for research and the rare opportunity for in-depth exchange of views from industry and academia constrain the development of mobile intelligent photography and imaging (MIPI). Building on the achievements of the previous MIPI Workshops held at ECCV 2022 and cvpr 2023, we introduce our third MIPI challenge including three tracks focusing on novel image sensors and imaging algorithms. In this paper, we summarize and review the Nighttime Flare Removal track on MIPI 2024. In total, 170 participants were successfully registered, and 14 teams submitted results in the final testing phase. The developed solutions in this challenge achieved state-of-the-art performance on Nighttime Flare Removal. More details of this challenge and the link to the dataset can be found at https://***/MIPI2024.

关键词： Photography Image sensors Industries computer vision Reviews conferences Focusing

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Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh Effect Rendering from a Single Image

Stacked Deep Multi-Scale Hierarchical Network for Fast Bokeh...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Dutta, Saikat Das, Sourya Dipta Shah, Nisarg A. Tiwari, Anil Kumar IIT Madras Chennai Tamil Nadu India Jadavpur Univ Kolkata India IIT Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9781665448994

The Bokeh Effect is one of the most desirable effects in photography for rendering artistic and aesthetic photos. Usually, it requires a DSLR camera with different aperture and shutter settings and certain photography skills to generate this effect. In smartphones, computational methods and additional sensors are used to overcome the physical lens and sensor limitations to achieve such effect. Most of the existing methods utilized additional sensor's data or pre-trained network for fine depth estimation of the scene and sometimes use portrait segmentation pretrained network module to segment salient objects in the image. Because of these reasons, networks have many parameters, become runtime intensive and unable to run in mid-range devices. In this paper, we used an end-to-end Deep Multi-Scale Hierarchical Network (DMSHN) model for direct Bokeh effect rendering of images captured from the monocular camera. To further improve the perceptual quality of such effect, a stacked model consisting of two DMSHN modules is also proposed. Our model does not rely on any pretrained network module for Monocular Depth Estimation or Saliency Detection, thus significantly reducing the size of model and run time. Stacked DMSHN achieves state-of-the-art results on a large scale EBB! dataset with around 6x less runtime compared to the current state-of-the-art model in processing HD quality images.

关键词： Photography Image segmentation Runtime Estimation Rendering (computer graphics) Cameras Real-time systems

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Learning from the Web: Webly Supervised Meta-Learning for Masked Face recognition

Learning from the Web: Webly Supervised Meta-Learning for Ma...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zheng, Wenbo Yan, Lan Wang, Fei-Yue Gou, Chao Xi An Jiao Tong Univ Sch Software Engn Xian Peoples R China Sun Yat Sen Univ Sch Intelligent Syst Engn Guangzhou Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China

ISBN: (纸本)9781665448994

Mask wearing has been considered as an effective measure to prevent the spread of COVID-19 during the current pandemic. However, most advanced face recognition approaches are not adequate for masked face recognition, particularly in dealing with the issue of training through the datasets covering only a limited number of images with ground-truth labels. In this work, we propose to learn from the large scale of web images and corresponding tags without any manual annotations along with limited fully annotated datasets. In particular, inspired by the recent success of webly supervised learning in deep neural networks, we capitalize on readily-available web images with noisy annotations to learn a robust representation for masked faces. Besides, except for the conventional spatial representation learning, we propose to leverage the power of frequency domain to capture the local representative information of unoccluded facial parts. This approach learns robust feature embeddings derived from our feature fusion architecture to make joint and full use of information from both spatial and frequency domains. Experimental results on seven benchmarks show that the proposed approach significantly improves the performance compared with other state-of-theart methods.

关键词： Knowledge engineering Training Annotations Pandemics Face recognition Frequency-domain analysis Supervised learning

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NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset,...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Perez-Pellitero, Eduardo Catley-Chandar, Sibi Leonardis, Ales Timofte, Radu Wang, Xian Li, Yong Wang, Tao Song, Fenglong Liu, Zhen Lin, Wenjie Li, Xinpeng Rao, Qing Jiang, Ting Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Chen, Xiangyu Liu, Yihao Zhang, Zhengwen Qiao, Yu Dong, Chao Chee, Evelyn Yi Lyn Shen, Shanlan Duan, Yubo Chen, Guannan Sun, Mengdi Gao, Yan Zhang, Lijie Akhil, K. A. Jiji, C., V Sharif, S. M. A. Naqvi, Rizwan Ali Biswas, Mithun Kim, Sungjun Xia, Chenjie Zhao, Bowen Ye, Zhangyu Lu, Xiwen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Rosh, Green K. S. Lomte, Sachin Deepak Krishnan, Nikhil Prasad, B. H. Pawan Huawei Noahs Ark Lab Shenzhen Peoples R China Megvii Technol Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Black Sesame Technol Singapore Singapore BOE Technol Co Ltd Beijing Peoples R China Coll Engn Trivandrum Trivandrum Kerala India Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China Zhejiang Univ Key Lab Adv Mfg Technol Hangzhou Peoples R China Sejong Univ Seoul South Korea Rigel IT Dhaka Bangladesh Samsung R&D Inst India Bangalore SRI B Bangalore Karnataka India

ISBN: (纸本)9781665448994

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with cvpr 2021. This manuscript focuses on the newly introduced dataset, the proposed methods and their results. The challenge aims at estimating a HDR image from one or multiple respective low-dynamic range (LDR) observations, which might suffer from underor over-exposed regions and different sources of noise. The challenge is composed by two tracks: In Track 1 only a single LDR image is provided as input, whereas in Track 2 three differently-exposed LDR images with inter-frame motion are available. In both tracks, the ultimate goal is to achieve the best objective HDR reconstruction in terms of PSNR with respect to a ground-truth image, evaluated both directly and with a canonical tonemapping operation.

关键词： computer vision Tracking conferences Imaging Dynamic range Market research pattern recognition

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