GB-LSR: A Fast Local Spectral Image Representation with a Single Global Bandwidth for Continuous Reconstruction and Super-Resolution
arXiv:2606.19617v1 Announce Type: cross Abstract: We present GB-LSR (Global-Bandwidth Local Spectral Representation), a fixed-grid local spectral representation for continuous image reconstruction. The image domain is partitioned into non-overlapping square patches, each carrying coefficients for a truncated Fourier basis predicted from shared convolutional-encoder features. A single trainable scalar bandwidth is shared globally across all patches and images, and reconstruction at any continuous coordinate is a fixed-size basis contraction whose cost is independent of image size. We study thre
This paper introduces an innovative approach to image representation that promises enhanced efficiency for continuous image reconstruction, published as the field continues to seek more performant and computationally less intensive methods.
A fast, globally-bandwidth-controlled local spectral image representation could lead to significant advancements in image processing, impacting areas like AI model training, computer vision applications, and efficient data handling.
The computational cost of image reconstruction at continuous coordinates can become independent of image size, potentially accelerating AI model inference and enabling more sophisticated real-time vision systems.
- · AI/ML developers
- · Computer vision companies
- · Cloud computing providers
- · Graphics processing unit manufacturers
Improved efficiency in image processing tasks for AI applications becomes widely accessible.
New AI models requiring high-resolution continuous image data become more practical, driving innovation in areas like medical imaging and autonomous navigation.
The reduced computational burden for image tasks could lower the barrier to entry for certain AI development, fostering a more diverse competitive landscape.
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Read at arXiv cs.LG