  Most existing methods for CRF estimation from a single image fail to handle
general real images. For instance, EdgeCRF based on colour patches extracted
from edges works effectively only when the presence of noise is insignificant,
which is not the case for many real images; and, CRFNet, a recent method based
on fully supervised deep learning works only for the CRFs that are in the
training data, and hence fail to deal with other possible CRFs beyond the
training data. To address these problems, we introduce a non-deep-learning
method using prediction consistency and gradual refinement. First, we rely more
on the patches of the input image that provide more consistent predictions. If
the predictions from a patch are more consistent, it means that the patch is
likely to be less affected by noise or any inferior colour combinations, and
hence, it can be more reliable for CRF estimation. Second, we employ a gradual
refinement scheme in which we start from a simple CRF model to generate a
result which is more robust to noise but less accurate, and then we gradually
increase the model's complexity to improve the result. This is because a simple
model, while being less accurate, overfits less to noise than a complex model
does. Our experiments show that our method outperforms the existing
single-image methods for daytime and nighttime real images. We further propose
a more efficient deep learning extension that performs test-time training
(based on unsupervised losses) on the test input image. This provides our
method better generalization performance than CRFNet making it more practically
applicable for CRF estimation for general real images.
