Abstract. Existing deblurring methods mainly focus on developing effective image priors and assume that blurred images contain insignificant
amounts of noise. However, state-of-the-art deblurring methods do not
perform well on real-world images degraded with significant noise or
outliers. To address these issues, we show that it is critical to learn data
fitting terms beyond the commonly used ?1 or ?2 norm. We propose a
simple and effective discriminative framework to learn data terms that
can adaptively handle blurred images in the presence of severe noise and
outliers. Instead of learning the distribution of the data fitting errors, we
directly learn the associated shrinkage function for the data term using a
cascaded architecture, which is more flexible and efficient. Our analysis
shows that the shrinkage functions learned at the intermediate stages can
effectively suppress noise and preserve image structures. Extensive experimental results show that the proposed algorithm performs favorably
against state-of-the-art methods