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Lieferstatus:	i.d.R. innert 5-10 Tagen versandfertig
Veröffentlichung:	November 2022
Genre:	EDV / Informatik
	Applications / computerscience / ConferenceProceedings / Informatics / Research
ISBN:	9783031197994
EAN-Code:	9783031197994
Verlag:	Springer
Einband:	Kartoniert
Sprache:	English
Dimensionen:	H 235 mm / B 155 mm / D 44 mm
Gewicht:	1218 gr
Seiten:	820
Bewertung:	Titel bewerten / Meinung schreiben

Inhalt:

Learning Mutual Modulation for Self-Supervised Cross-Modal Super Resolution.- Spectrum-Aware and Transferable Architecture Search for Hyperspectral Image Restoration.- Neural Color Operators for Sequential Image Retouching.- Optimizing Image Compression via Joint Learning with Denoising.- Restore Globally, Refine Locally: A Mask-Guided Scheme to Accelerate Super-Resolution Networks.- Compiler-Aware Neural Architecture Search for On-Mobile Real-Time Super-Resolution.- Modeling Mask Uncertainty in Hyperspectral Image Reconstruction.- Perceiving and Modeling Density for Image Dehazing.- Stripformer: Strip Transformer for Fast Image Deblurring.- Deep Fourier-Based Exposure Correction Network with Spatial-Frequency Interaction.- Frequency and Spatial Dual Guidance for Image Dehazing.- Towards Real-World HDRTV Reconstruction: A Data Synthesis Based Approach.- Learning Discriminative Shrinkage Deep Networks for Image Deconvolution.- KXNet: A Model-Driven Deep Neural Network for Blind Super Resolution.- ARM: Any-Time Super-Resolution Method.- Attention-Aware Learning for Hyperparameter Prediction in Image Processing Pipelines.- RealFlow: EM-Based Realistic Optical Flow Dataset Generation from Videos.- Memory-Augmented Model-Driven Network for Pansharpening.- All You Need Is RAW: Defending against Adversarial Attacks with Camera Image Pipelines.- Ghost-Free High Dynamic Range Imaging with Context-Aware Transformer.- Style-Guided Shadow Removal .- D2C-SR: A Divergence to Convergence Approach for Real-World Image Super-Resolution.- GRIT-VLP: Grouped Mini-Batch Sampling for Efficient Vision and Language Pre-training.- Efficient Video Deblurring Guided by Motion Magnitude.- Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and a New Physics-Inspired Transformer Model .- Contextformer: A Transformer with Spatio-Channel Attention for Context Modeling in Learned Image Compression.- Image Super-Resolution with Deep Dictionary.- TempFormer: Temporally Consistent Transformer for Video Denoising.- RAWtoBit: A Fully End-to-End Camera ISP Network.- DRCNet: Dynamic Image Restoration Contrastive Network.- Zero-Shot Learning for Reflection Removal of Single 360-Degree Image.- Transformer with Implicit Edges for Particle-Based Physics Simulation.- Rethinking Video Rain Streak Removal: A New Synthesis Model and a Deraining Network with Video Rain Prior.- Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images.- Animation from Blur: Multi-modal Blur Decomposition with Motion Guidance.- AlphaVC: High-Performance and Efficient Learned Video Compression.- Content-Oriented Learned Image Compression.- RRSR:Reciprocal Reference-Based Image Super-Resolution with Progressive Feature Alignment and Selection.- Contrastive Prototypical Network with Wasserstein Confidence Penalty.- Learn to-Decompose: Cascaded Decomposition Network for Cross-Domain Few-Shot Facial Expression Recognition.- Self-Support Few-Shot Semantic Segmentation.- Few-Shot Object Detection with Model Calibration.- Self-Supervision Can Be a Good Few-Shot Learner.