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NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

NTIRE 2023 HR NonHomogeneous Dehazing Challenge Report

引用

2023 IEEE/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Liu, Huan Li, Liangyan Wu, Zijun Dong, Yubo Li, Yuyan Qiu, Tian He, Yu Lu, Yonghong Wu, Yinwei Jiang, Zhenxiang Liu, Songhua Yang, Xingyi Jing, Yongcheng Benjdira, Bilel Ali, Anas M. Koubaa, Anis Yang, Hao-Hsiang Chen, I-Hsiang Chen, Wei-Ting Huang, Zhi-Kai Chen, Yi-Chung Hsieh, Chia-Hsuan Chang, Hua-En Chiang, Yuan-Chun Kuo, Sy-Yen Guo, Yu Gao, Yuan Liu, Ryan Wen Lu, Yuxu Qu, Jingxiang He, Shengfeng Ren, Wenqi Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Yang, Lehan Wu, Alex Jiahao Mai, Tiancheng Cong, Xiaofeng Yin, Xuemeng Yin, Xuefei Emad, Hazim Abdallah, Ahmed Yasser, Yahya Elshahat, Dalia Elbaz, Esraa Li, Zhan Kuang, Wenqing Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhang, Zhao Wei, Yanyan Wang, Junhu Zhao, Suiyi Zheng, Huan Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Jiang, Kui Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph ETcTI Universitatea Politehnica Timisoara Romania ICTEAM UCL Belgium Computer Vision Lab University of Wuerzburg Germany Computer Vision Lab ETH Zurich Switzerland Department of Electrical and Computer Engineering McMaster University Canada Department of Electrical and Computer Engineering University of Alberta Canada McMaster University Canada Xidian University China Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Wuhan University of Technology China Singapore Management University Singapore Singapore Sun Yat-sen University China Electrical Engineering Department Pennsylvania State University United States The University of Sydney Australia Southeast University China University of California Los Angeles United States Beijing Jiaotong University China Mansoura Univeristy Egypt College of Information Science and Technology Jinan University China Department of Information Technology Uppsala University Sweden Hefei University of Technology China Zhejiang Dahua Technology China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology. © 2023 IEEE.

关键词： Demulsification

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Efficient local correlation volume for unsupervised optical flow estimation on small moving objects in large satellite images

Efficient local correlation volume for unsupervised optical ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Sarra Khairi Etienne Meunier Renaud Fraisse Patrick Bouthemy Inria Rennes France Airbus Defence & Space Toulouse France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With the advent of deep learning methods, performance and efficiency of optical flow estimation has significantly increased, especially for supervised models. However, they do not generalize well to more specific data involving small moving objects in large images, such as high-resolution aerial or satellite sequences. In addition, annotation and realistic simulation are difficult for these contents, which calls for unsupervised alternatives. Yet, the latter are still less accurate than their supervised counterparts. In this paper, we introduce an unsupervised local optical flow estimation method adapted to small moving objects in large-size images by involving no downsampling of the feature maps. We adopt a local correlation search and implement it in an original way with a per-shift computation, which minimizes memory consumption and speed up inference computation for large-scale images. We also design a loss function combining similarity, smoothness and sparsity constraints. We demonstrate the performance of our SMOFlow method on real stabilized aerial videos fully representative of future satellite conditions. SMOFlow favorably compares to other methods. Our SMOFlow method is able to accurately capture the motion of small objects in large images, while efficiently reducing memory consumption.

关键词： computer vision Solid modeling Correlation Satellites Memory management Estimation Satellite images

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An Information Theoretic Approach for Active and Effective Object recognitions

An Information Theoretic Approach for Active and Effective O...

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International conference on pattern recognition

作者： K. Deguchi H. Ohtsu Graduate School of Information Sciences University of Tohoku Sendai Japan

This paper describes the strategy of optimal action selection for recognition of the observed object among those registered in the image data base from an information theoretic point of view. The goal is to reduce the number and effort of the recognition steps to reach the final decision of the recognition. We have chosen an information theoretic framework, motivated by the fact that sensored image data is not noiseless or ideal, nor can the effect of a certain action be completely determined in advance. We present the strategy of the recognition process, and then, the results of the feasibility study by experiments

关键词： Cameras Object recognition Image databases Image recognition Robot vision systems Uncertainty computer vision Probability distribution State estimation Machine vision

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Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR Scans

Multi-Layer Modeling of Dense Vegetation from Aerial LiDAR S...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ekaterina Kalinicheva Loic Landrieu Clé ment Mallet Nesrine Chehata ENSG IGN LASTIG Universit&#x00E9 Gustave Eiffel Marne-la-Vallee France INRAE UMR 1202 BIOGECO Universit&#x00E9 de Bordeaux France Bordeaux INP Universit&#x00E9 Bordeaux Montaigne France

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

The analysis of the multi-layer structure of wild forests is an important challenge of automated large-scale forestry. While modern aerial LiDARs offer geometric information across all vegetation layers, most datasets and methods focus only on the segmentation and reconstruction of the top of canopy. We release WildForest3D, which consists of 29 study plots and over 2000 individual trees across 47 000m 2 with dense 3D annotation, along with occupancy and height maps for 3 vegetation layers: ground vegetation, understory, and overstory. We propose a 3D deep network architecture predicting for the first time both 3D point-wise labels and high-resolution layer occupancy rasters simultaneously. This allows us to produce a precise estimation of the thickness of each vegetation layer as well as the corresponding watertight meshes, therefore meeting most forestry purposes. Both the dataset and the model are released in open access: https://***/ekalinicheva/multi_layer_vegetation.

关键词： computer vision Solid modeling Three-dimensional displays Laser radar Annotations Atmospheric modeling Time series analysis

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Fully Test-time Adaptation for Object Detection

Fully Test-time Adaptation for Object Detection

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xiaoqian Ruan Wei Tang University of Illinois Chicago

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Though the object detection performance on standard benchmarks has been improved drastically in the last decade, current object detectors are often vulnerable to domain shift between the training data and testing images. Domain adaptation techniques have been developed to adapt an object detector trained in a source domain to a target domain. However, they assume that the target domain is known and fixed and that a target dataset is available for training, which cannot be satisfied in many real-world applications. To close this gap, this paper investigates fully test-time adaptation for object detection. It means to update a trained object detector on a single testing image before making a prediction, without access to the training data. Through a diagnostic study of a baseline self-training framework, we show that a great challenge of this task is the unreliability of pseudo labels caused by domain shift. We then propose a simple yet effective method, termed the IoU Filter, to address this challenge. It consists of two new IoU-based indicators, both of which are complementary to the detection confidence. Experimental results on five datasets demonstrate that our approach could effectively adapt a trained detector to various kinds of domain shifts at test time and bring substantial performance gains. Code is available at https://***/XiaoqianRuan1/IoU-filter.

关键词： Training computer vision Codes conferences Training data Detectors Object detection

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Learnable Global Spatio-Temporal Adaptive Aggregation for Bracketing Image Restoration and Enhancement

Learnable Global Spatio-Temporal Adaptive Aggregation for Br...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xinwei Dai Yuanbo Zhou Xintao Qiu Hui Tang Wei Deng Qingquan Gao Tong Tong Fuzhou University Imperial Vision Technology

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Employing specific networks to address different types of degradation often proved to be complex and time-consuming in practical applications. The Bracket Image Restoration and Enhancement (BIRE) aimed to address various image restoration tasks in a unified manner by restoring clear single-frame images from multiple-frame shots, including denoising, deblurring, enhancing high dynamic range (HDR), and achieving super-resolution under various degradation conditions. In this paper, we propose LGSTANet, an efficient aggregation restoration network for BIRE. Specifically, inspired by video restoration methods, we adopt an efficient architecture comprising alignment, aggregation, and reconstruction. Additionally, we introduce a Learnable Global Spatio-Temporal Adaptive (LGSTA) aggregation module to effectively aggregate inter-frame complementary information. Furthermore, we propose an adaptive restoration modulator to address specific degradation disturbances of various types, thereby achieving high-quality restoration outcomes. Extensive experiments demonstrate the effectiveness of our method. LGSTANet outperforms other state-of-the-art methods in Bracket Image Restoration and Enhancement and achieves competitive results in the NTIRE2024 BIRE challenge.

关键词： Degradation Aggregates Perturbation methods Superresolution Noise reduction Modulation computer architecture

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Latent Flow Diffusion for Deepfake Video Generation

Latent Flow Diffusion for Deepfake Video Generation

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Aashish Chandra K Aashutosh A V Srijan Das Abhijit Das Birla Institute of Technology & Science Pilani India University of North Carolina Charlotte USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Image-to-video generation with conditional identity swap popularly known as deepfake, aims to synthesize a new video for the target identity guided by an image of the target and a video of the source identity. The biggest challenge of these tasks lies in the simultaneous generation of realistic spatial appearance and temporal dynamics corresponding to the given target image and source video. In this paper, we propose a deepfake generation technique using novel latent flow diffusion (LFD) that includes an optical flow sequence in the latent space based on a given source video to warp the given target image. Compared to previous works on video diffusion, our proposed LFD can swap the spatial details maintaining temporal information by utilizing the spatial content of the given target image and employing the latent flow of the source video. Our model consists of three stages: a Flow predictor model captures the optical flow of the source video, two-fold Transformer encoding layers predict the driving frame and a conditioned image-to-video generator guided by the driving frame generates the final deep fake video. We conducted multiple experiments and our proposed model has consistently outperformed prior video diffusion models for deepfake generation.

关键词： Deepfakes computer vision Image motion analysis Image coding Predictive models Transformers Generators

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SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-Image Translation while Maintaining Stereo Constraint

SyntStereo2Real: Edge-Aware GAN for Remote Sensing Image-to-...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Vasudha Venkatesan Daniel Panangian Mario Fuentes Reyes Ksenia Bittner University of Freiburg German Aerospace Center (DLR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of remote sensing, the scarcity of stereo-matched and particularly lack of accurate ground truth data often hinders the training of deep neural networks. The use of synthetically generated images as an alternative, alleviates this problem but suffers from the problem of domain generalization. Unifying the capabilities of image-to-image translation and stereo-matching presents an effective solution to address the issue of domain generalization. Current methods involve combining two networks—an unpaired image-to-image translation network and a stereo-matching network—while jointly optimizing them. We propose an edge-aware GAN-based network that effectively tackles both tasks simultaneously. We obtain edge maps of input images from the Sobel operator and use it as an additional input to the encoder in the generator to enforce geometric consistency during translation. We additionally include a warping loss calculated from the translated images to maintain the stereo consistency. We demonstrate that our model produces qualitatively and quantitatively superior results than existing models, and its applicability extends to diverse domains, including autonomous driving.

关键词： Training computer vision Accuracy Image edge detection conferences Generative adversarial networks Generators

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How Do Movie Viewers Perceive Scene Structure from Dynamic Cues

How Do Movie Viewers Perceive Scene Structure from Dynamic C...

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conference on computer vision and pattern recognition (CVPR)

作者： Loong-Fah Cheong Xu Xiang Department of Electrical and Computer Engineering National University of Singapore Singapore

Cinema viewed from a location other than a Canonical Viewing Point (CVP) presents distortions to the viewer in both its static and dynamic aspects. Past works have investigated mainly the static aspect of this problem and attempted to explain why viewers still seem to perceive the scene very well. The dynamic aspect of depth perception, which is known as structure from motion, and its possible distortion, have not been well investigated. In this paper, we derive the dynamic depth cues perceived by the viewer and use the so-called isodistortion framework to understand its distortion. The result is that viewers seated at a reasonably central position experience a shift in the intrinsic parameters of their visual systems. Despite this shift, the key properties of the perceived depths remain largely the same, being determined in the main by the accuracy to which extrinsic motion parameters can be recovered. For a viewer seated at a non-central position and watching the movie screen with a slant angle, the view is related to the view at the CVP by a homography, resulting in various aberrations such as non-central projection.

关键词： Motion pictures Layout Retina Shape computer errors Motion analysis Motion estimation Drives Visual system Eyes

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Textured 3D face recognition using biological vision-based facial representation and optimized weighted sum fusion

Textured 3D face recognition using biological vision-based f...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Di Huang Wael Ben Soltana Mohsen Ardabilian Yunhong Wang Liming Chen Ecole Centrale Lyon LIRIS UMR 5205CNRS Université de Lyon Ecully France School of Computer Science and Engineering Beihang University Beijing China

This paper proposes a novel biological vision-based facial description, namely Perceived Facial Images (PFIs), aiming to highlight intra-class and inter-class variations of both facial range and texture images for textured 3D face recognition. These generated PFIs simulate the response of complex neurons to gradient information within a certain neighborhood and possess the properties of being highly distinctive and robust to affine illumination and geometric transformation. Based on such an intermediate facial representation, SIFT-based matching is further carried out to calculate similarity scores between a given probe face and the gallery ones. Because the facial description generates a PFI for each quantized gradient orientation of range and texture faces, we then propose a score level fusion strategy which optimizes the weights using a genetic algorithm in a learning step. Evaluated on the entire FRGC v2.0 database, the rank-one recognition rate using only 3D or 2D modality is 95.5% and 95.9%, respectively; while fusing both modalities, i.e. range and texture-based PFIs, the final accuracy is 98.0%, demonstrating the effectiveness of the proposed biological vision-based facial description and the optimized weighted sum fusion.

关键词： Face Three dimensional displays Face recognition Neurons Lighting Solid modeling

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