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Title: WIRE: Wavelet Implicit Neural Representations
Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of activation function employed in its MLP network. A wide range of nonlinearities have been explored, but, unfortunately, current INRs designed to have high accuracy also suffer from poor robustness (to signal noise, parameter variation, etc.). Inspired by harmonic analysis, we develop a new, highly accurate and robust INR that does not exhibit this tradeoff. Our Wavelet Implicit neural REpresentation (WIRE) uses as its activation function the complex Gabor wavelet that is well-known to be optimally concentrated in space–frequency and to have excellent biases for representing images. A wide range of experiments (image denoising, image inpainting, super-resolution, computed tomography reconstruction, image overfitting, and novel view synthesis with neural radiance fields) demonstrate that WIRE defines the new state of the art in INR accuracy, training time, and robustness.  more » « less
Award ID(s):
2107313
NSF-PAR ID:
10431453
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN:
1063-6919
Page Range / eLocation ID:
18507-18516
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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