Image Superresolution under Spatially Structured Noise

We develop an image superresolution method that can deal with spatially structured noise added to an original image. Such a structured noise process can be understood as a model for possible occlusions such as clouds in the sky or stains on the lens, and is modeled as spin glasses. The original high-resolution image underlying multiple low-resolution observed images and the hidden noise structure are estimated via a variational learning algorithm. Experiments show that our superresolution method can outperform other methods that do not assume structured noise.