Search Results for author:
Found 9 papers, 2 papers with code
ICFRNet: Image Complexity Prior Guided Feature Refinement for Real-time Semantic Segmentation
In this paper, we leverage image complexity as a prior for refining segmentation features to achieve accurate real-time semantic segmentation.
Spread Your Wings: A Radial Strip Transformer for Image Deblurring
This attention module contains radial strip windows to reweight image features in the polar coordinate, which preserves more useful information in rotation and translation motion together for better recovering the sharp images.
Adapt or Perish: Adaptive Sparse Transformer with Attentive Feature Refinement for Image Restoration
Meanwhile FRFN employs an enhance-and-ease scheme to eliminate feature redundancy in channels enhancing the restoration of clear latent images.
Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression
Light field is a type of image data that captures the 3D scene information by recording light rays emitted from a scene at various orientations.
Learning-based Spatial and Angular Information Separation for Light Field Compression
Light fields are a type of image data that capture both spatial and angular scene information by recording light rays emitted by a scene from different orientations.
JAWS: Just A Wild Shot for Cinematic Transfer in Neural Radiance Fields
This paper presents JAWS, an optimization-driven approach that achieves the robust transfer of visual cinematic features from a reference in-the-wild video clip to a newly generated clip.
Distilled Low Rank Neural Radiance Field with Quantization for Light Field Compression
While existing compression methods encode the set of light field sub-aperture images, our proposed method learns an implicit scene representation in the form of a Neural Radiance Field (NeRF), which also enables view synthesis.
A learning-based view extrapolation method for axial super-resolution
Axial light field resolution refers to the ability to distinguish features at different depths by refocusing.
Learning Fused Pixel and Feature-Based View Reconstructions for Light Fields
In this paper, we present a learning-based framework for light field view synthesis from a subset of input views.
