  Many computer vision applications, such as object recognition and
segmentation, increasingly build on superpixels. However, there have been so
far few superpixel algorithms that systematically deal with noisy images. We
propose to first decompose the image into equal-sized rectangular patches,
which also sets the maximum superpixel size. Within each patch, a Potts model
for simultaneous segmentation and denoising is applied, that guarantees
connected and non-overlapping superpixels and also produces a denoised image.
The corresponding optimization problem is formulated as a mixed integer linear
program (MILP), and solved by a commercial solver. Extensive experiments on the
BSDS500 dataset images with noises are compared with other state-of-the-art
superpixel methods. Our method achieves the best result in terms of a combined
score (OP) composed of the under-segmentation error, boundary recall and
compactness.
