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Found 6 papers, 1 papers with code
Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation
To address these challenges, we propose to colorize NIR images via a multi-scale progressive feature embedding network (MPFNet), with the guidance of grayscale image colorization.
Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues
Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands.
Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation
To address these challenges, we propose a cooperative learning paradigm that colorizes NIR images in parallel with another proxy grayscale colorization task by exploring latent cross-domain priors (i. e., latent spectrum context priors and task domain priors), dubbed CoColor.
Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues
Finally, a Fusion Attention Block (FAB) is proposed to adaptively fuse the features from the two branches and generate an optimized colorization result.
Attention-Guided Progressive Neural Texture Fusion for High Dynamic Range Image Restoration
A progressive texture blending module is designed to blend the encoded two-stream features in a multi-scale and progressive manner.
Scale-Consistent Fusion: from Heterogeneous Local Sampling to Global Immersive Rendering
Image-based geometric modeling and novel view synthesis based on sparse, large-baseline samplings are challenging but important tasks for emerging multimedia applications such as virtual reality and immersive telepresence.
