Exploring Invariance in Images through One-way Wave Equations
This provides a novel perspective for image analysis, potentially benefiting computer vision researchers, though it appears incremental as it builds on existing encoder-decoder frameworks.
The paper tackles the problem of understanding image structure by revealing an invariance where images share a set of one-way wave equations with latent speeds, enabling high-fidelity reconstruction from a single initial condition vector.
In this paper, we empirically reveal an invariance over images-images share a set of one-way wave equations with latent speeds. Each image is uniquely associated with a solution to these wave equations, allowing for its reconstruction with high fidelity from an initial condition. We demonstrate it using an intuitive encoder-decoder framework where each image is encoded into its corresponding initial condition (a single vector). Subsequently, the initial condition undergoes a specialized decoder, transforming the one-way wave equations into a first-order norm + linear autoregressive process. This process propagates the initial condition along the x and y directions, generating a high-resolution feature map (up to the image resolution), followed by a few convolutional layers to reconstruct image pixels. The revealed invariance, rooted in the shared wave equations, offers a fresh perspective for comprehending images, establishing a promising avenue for further exploration.