WIRE: Wavelet Implicit Neural Representations

• URL: http://arxiv.org/abs/2301.05187v1
• Date: Thu, 5 Jan 2023 20:24:56 GMT
• Title: WIRE: Wavelet Implicit Neural Representations
• Authors: Vishwanath Saragadam, Daniel LeJeune, Jasper Tan, Guha Balakrishnan, Ashok Veeraraghavan, Richard G. Baraniuk
• Abstract summary: Implicit neural representations (INRs) have recently advanced numerous vision-related areas. Current INRs designed to have high accuracy also suffer from poor robustness. We develop a new, highly accurate and robust INR that does not exhibit this tradeoff.
• Score: 42.147899723673596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Implicit neural representations (INRs) have recently advanced numerous vision-related areas. INR performance depends strongly on the choice of the nonlinear activation function employed in its multilayer perceptron (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. Wavelet Implicit neural REpresentation (WIRE) uses a continuous complex Gabor wavelet activation function 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.

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