Novel integro-differential equations in image processing and its applications

Motivated by the hierarchical multiscale image representation of Tadmor et al., we propose a novel integrodifferential equation (IDE) for a multiscale image representation. To this end, one integrates in inverse scale space a succession of refined, recursive ‘slices’ of the image, which are balanced by a typical curvature term at the finer scale. Although the original motivation came from a variational approach, the resulting IDE can be extended using standard techniques from PDE-based image processing. We use filtering, edge preserving smoothing to yield a family of modified IDE models with applications to image denoising and image deblurring problems. The IDE models depend on a user scaling function which is shown to dictate the BV ∗ properties of the residual error. Numerical experiments demonstrate application of the IDE approach to denoising and deblurring. Finally, we also propose another novel IDE based on the (BV,L) decomposition. We present numerical results for this IDE and its variant and examine its properties.