Progressive Image Super-Resolution via Neural Differential Equation
This provides a clearer and more flexible approach for image enhancement, though it appears incremental in method.
The authors tackled image super-resolution by formulating it as an initial value problem using neural ordinary differential equations, achieving superior performance over state-of-the-art methods.
We propose a new approach for the image super-resolution (SR) task that progressively restores a high-resolution (HR) image from an input low-resolution (LR) image on the basis of a neural ordinary differential equation. In particular, we newly formulate the SR problem as an initial value problem, where the initial value is the input LR image. Unlike conventional progressive SR methods that perform gradual updates using straightforward iterative mechanisms, our SR process is formulated in a concrete manner based on explicit modeling with a much clearer understanding. Our method can be easily implemented using conventional neural networks for image restoration. Moreover, the proposed method can super-resolve an image with arbitrary scale factors on continuous domain, and achieves superior SR performance over state-of-the-art SR methods.