Pattern recognition and computer vision . Part III : 4th Chinese Conference, PRCV 2021, Beijing, China, October 29-November 1, 2021, Proceedings / / Huimin Ma [and seven others] (editors)

• Pubblicazione: Cham, Switzerland : , : Springer, , 2022; ©2021
• Descrizione fisica: 1 online resource (648 pages)
• Disciplina: 006.4
• Soggetto topico: Pattern recognition systems; Computer vision
• Persona (resp. second.): MaHuimin
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Contents - Part III -- Low-Level Vision and Image Processing -- SaliencyBERT: Recurrent Attention Network for Target-Oriented Multimodal Sentiment Classification -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Task Definition -- 3.2 Recurrent Attention Network -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Results and Analysis -- 5 Conclusion -- References -- Latency-Constrained Spatial-Temporal Aggregated Architecture Search for Video Deraining -- 1 Introduction -- 2 The Proposed Method -- 2.1 Spatial-Temporal Aggregated Architecture -- 2.2 Architecture Search -- 3 Experimental Results -- 3.1 Experiment Preparation -- 3.2 Running Time Evaluation -- 3.3 Quantitative Comparison -- 3.4 Qualitative Comparison -- 3.5 Ablation Study -- 4 Conclusions -- References -- Semantic-Driven Context Aggregation Network for Underwater Image Enhancement -- 1 Introduction -- 2 Method -- 2.1 The Overall Architecture -- 2.2 Semantic Feature Extractor -- 2.3 Multi-scale Feature Transformation Module -- 2.4 Context Aggregation Enhancement Network and Loss Function -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Comparison with the State-of-the-Arts -- 3.3 Ablation Study -- 3.4 Application on Salient Object Detection -- 4 Conclusion -- References -- A Multi-resolution Medical Image Fusion Network with Iterative Back-Projection -- 1 Introduction -- 2 Proposed Approach -- 2.1 Overall Framework -- 2.2 Network Architecture -- 2.3 Loss Function -- 3 Experiments -- 3.1 Dataset and Training Details -- 3.2 Results and Analysis of IBPNet -- 4 Conclusions -- References -- Multi-level Discriminator and Wavelet Loss for Image Inpainting with Large Missing Area -- 1 Introduction -- 2 Related Work -- 2.1 Image Inpainting -- 2.2 Adversarial Training -- 3 Our Approach -- 3.1 Multi-level Discriminator -- 3.2 Wavelet Loss. ; 4 Experiments -- 4.1 Experimental Settings -- 4.2 Performance Evaluation -- 4.3 Ablation Study -- 5 Conclusion -- References -- 3D2Unet: 3D Deformable Unet for Low-Light Video Enhancement -- 1 Introduction -- 2 Related Work -- 2.1 Low-Light Image Enhancement -- 2.2 Low-Light Video Enhancement -- 3 Method -- 3.1 Problem Formulation -- 3.2 Overview of the Pipeline -- 3.3 RSTAB Module -- 3.4 Unet Architecture with Global Projection -- 4 Experiment -- 4.1 Experimental Setting -- 4.2 Comparison with State-of-the-art Methods -- 4.3 Ablation Study -- 5 Conclusion -- References -- Single Image Specular Highlight Removal on Natural Scenes -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Scene Illumination Evaluation -- 3.2 Smooth Feature Extraction -- 3.3 Coefficient Estimation and Highlight Removal -- 4 Experiments -- 4.1 Quantitative Comparison on Laboratory Images -- 4.2 Visual Effect Comparison on Natural Scene Images -- 4.3 Discussion of Important Parameters -- 5 Conclusion -- References -- Document Image Binarization Using Visibility Detection and Point Cloud Segmentation -- 1 Introduction -- 2 TPO(Target-Point Occlusion) -- 2.1 Point Cloud Transformation -- 2.2 Convex Hull -- 3 Algorithm -- 3.1 Binarization -- 3.2 Unshadow Binarization -- 4 Experiment -- 5 Conclusion -- References -- LF-MAGNet: Learning Mutual Attention Guidance of Sub-Aperture Images for Light Field Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 2.1 Light Field Image Super-Resolution -- 2.2 Visual Attention Mechanism -- 3 Proposed Method -- 3.1 Shallow Feature Extraction -- 3.2 Mutual Attention Guidance -- 3.3 LF Image Reconstruction -- 4 Experiment -- 4.1 Dataset and Implementation Details -- 4.2 Ablation Studies -- 4.3 Comparisons with the State-of-The-Arts -- 5 Conclusion -- References. ; Infrared Small Target Detection Based on Weighted Variation Coefficient Local Contrast Measure -- 1 Introduction -- 2 The Proposed Algorithm -- 2.1 Variation Coefficient Local Contrast Measure -- 2.2 Weighted Variation Coefficient Local Contrast Measure -- 2.3 Target Detection -- 3 Experimental Results -- 3.1 Enhancement Performance Comparison -- 3.2 Detection Performance Comparison -- 4 Conclusion -- References -- Scale-Aware Distillation Network for Lightweight Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Scale-Aware Distillation Block -- 3.3 Comparisons with Other Information Distillation Methods -- 4 Experiments -- 4.1 Implementation Details -- 4.2 Ablation Study -- 4.3 Comparisons with the State-of-the-Arts -- 5 Conclusion -- References -- Deep Multi-Illumination Fusion for Low-Light Image Enhancement -- 1 Introduction -- 2 Deep Multi-Illumination Fusion -- 2.1 Network Structure -- 2.2 Loss Function -- 3 Experimental Results -- 3.1 Implementation Details -- 3.2 Performance Evaluation -- 3.3 Ablation Analysis -- 3.4 Object Instance Segmentation -- 4 Conclusion -- References -- Relational Attention with Textual Enhanced Transformer for Image Captioning -- 1 Introduction -- 2 Related Work -- 2.1 Relationship Exploration -- 2.2 Transformer Architecture -- 3 The Proposed Approach -- 3.1 Relation Module -- 3.2 Attention Module -- 3.3 Decoder Module -- 3.4 Training and Objectives -- 4 Experiments -- 4.1 Datasets and Evaluation Metrics -- 4.2 Implementation Details -- 4.3 Ablation Study -- 4.4 Comparison with State-of-the-Art -- 5 Conclusion -- References -- Non-local Network Routing for Perceptual Image Super-Resolution -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Non-local Network Routing -- 3.2 Model Learning -- 4 Experiments -- 4.1 Evaluation Dataset and Metric. ; 4.2 Implementation Details -- 4.3 Derived Architecture -- 4.4 Comparison with State-of-the-Art Methods -- 5 Conclusion -- References -- Multi-focus Image Fusion with Cooperative Image Multiscale Decomposition -- 1 Introduction -- 2 Cooperative Image Multiscale Decomposition Based MGF -- 2.1 Mutually Guided Filter -- 2.2 Cooperative Image Multiscale Decomposition -- 3 Image Fusion with CIMD -- 3.1 Base Layers Fusion -- 3.2 Detailed Layers Fusion -- 3.3 Reconstruction -- 4 Experiment -- 4.1 Experiment Setup -- 4.2 Comparison to Classical Fusion Method -- 5 Conclusions -- References -- An Enhanced Multi-frequency Learned Image Compression Method -- 1 Introduction -- 2 Related Works -- 3 Proposed Method -- 3.1 Formulation of Multi-frequency Learned Compression Models -- 3.2 Channel Attention Scheme -- 3.3 Decoder-Side Enhancement -- 4 Experiment Results -- 4.1 Parameter Description -- 4.2 Results Evaluation -- 5 Conclusion -- References -- Noise Map Guided Inpainting Network for Low-Light Image Enhancement -- 1 Introduction -- 2 Related Works -- 2.1 Low-Light Image Enhancement -- 2.2 Image Inpainting -- 3 Method -- 3.1 Stage I: Decomposition -- 3.2 Stage II: Restoration -- 4 Experiment -- 4.1 Implementation Details -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 5 Conclusion -- References -- FIE-GAN: Illumination Enhancement Network for Face Recognition -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 3.1 Architecture -- 3.2 Loss Function -- 3.3 Deployment in Face Recognition -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Visual Perception Results -- 4.3 Face Recognition Results -- 4.4 Ablation Study -- 5 Conclusion -- References -- Illumination-Aware Image Quality Assessment for Enhanced Low-Light Image -- 1 Introduction -- 2 Illumination-Aware Quality Assessment of Enhanced Low-Light Image -- 2.1 Intrinsic Decomposition Module. ; 2.2 CNN-based Feature Extraction Module -- 2.3 Learnable Perceptual Distance Measurement -- 3 Experiments -- 3.1 Basic Evaluations -- 3.2 Evaluations of LIE-IQA with Related Methods -- 3.3 LIE-IQA for Low-Light Enhancement -- 4 Conclusion -- References -- Smooth Coupled Tucker Decomposition for Hyperspectral Image Super-Resolution -- 1 Introduction -- 2 Tensor Notations and Preliminaries -- 3 Problem Formulation -- 3.1 Problem Description and Degradation Model -- 3.2 MAP Formulation -- 3.3 Smooth Coupled Tucker Decomposition Model -- 3.4 Optimization -- 4 Experimental Results -- 4.1 Experimental Settings and Implementation Issues -- 4.2 Experimental Results -- 4.3 Choice of Model Order -- 5 Conclusion -- 1. References -- Self-Supervised Video Super-Resolution by Spatial Constraint and Temporal Fusion -- 1 Introduction -- 2 Related Work -- 2.1 SISR -- 2.2 VSR -- 3 Methodology -- 3.1 Overview -- 3.2 Internal-Data Based VSR -- 3.3 Spatio-Temporal VSR -- 4 Experiments -- 4.1 Protocols -- 4.2 Real-World Testing -- 4.3 Ablation Study -- 5 Conclusion -- References -- ODE-Inspired Image Denoiser: An End-to-End Dynamical Denoising Network -- 1 Introduction -- 2 Related Work -- 2.1 Image Denoising with CNN -- 2.2 Neural ODEs V.S. Residual Learning -- 3 Proposed Method -- 3.1 Network Architecture -- 3.2 Problem Formulation -- 3.3 OI-Block -- 4 Experiments -- 4.1 Ablation Study -- 4.2 Synthetic Noisy Images -- 4.3 Real Noisy Images -- 5 Conclusion -- References -- Image Outpainting with Depth Assistance -- 1 Introduction -- 2 Related Work -- 3 Our Model -- 3.1 Framework Design -- 3.2 Training -- 4 Experiment -- 4.1 Dataset -- 4.2 Quantitative Evaluation -- 4.3 Qualitative Evaluation -- 4.4 Comparison of Depth Feature Extraction Solutions -- 5 Conclusion -- References -- Light-Weight Multi-channel Aggregation Network for Image Super-Resolution -- 1 Introduction. ; 2 Proposed Method.
• Titolo autorizzato: Pattern recognition and computer vision
• ISBN: 3-030-88010-9
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 996464422803316
• Lo trovi qui: Univ. di Salerno
• Serie: Lecture notes in computer science ; ; 13021.