A Single-Parameter Factor-Graph Image Prior
This work addresses image processing tasks like denoising and contrast enhancement, but appears incremental as it builds on existing factor-graph and prior-based methods without broad SOTA claims.
The authors tackled the problem of image denoising and contrast enhancement by proposing a piecewise smooth image model with automatically adapted local parameters, achieving results through factor graphs with NUP priors and iterative computations.
We propose a novel piecewise smooth image model with piecewise constant local parameters that are automatically adapted to each image. Technically, the model is formulated in terms of factor graphs with NUP (normal with unknown parameters) priors, and the pertinent computations amount to iterations of conjugate-gradient steps and Gaussian message passing. The proposed model and algorithms are demonstrated with applications to denoising and contrast enhancement.