  Enhancing visual qualities of images plays very important roles in various
vision and learning applications. In the past few years, both knowledge-driven
maximum a posterior (MAP) with prior modelings and fully data-dependent
convolutional neural network (CNN) techniques have been investigated to address
specific enhancement tasks. In this paper, by exploiting the advantages of
these two types of mechanisms within a complementary propagation perspective,
we propose a unified framework, named deep prior ensemble (DPE), for solving
various image enhancement tasks. Specifically, we first establish the basic
propagation scheme based on the fundamental image modeling cues and then
introduce residual CNNs to help predicting the propagation direction at each
stage. By designing prior projections to perform feedback control, we
theoretically prove that even with experience-inspired CNNs, DPE is definitely
converged and the output will always satisfy our fundamental task constraints.
The main advantage against conventional optimization-based MAP approaches is
that our descent directions are learned from collected training data, thus are
much more robust to unwanted local minimums. While, compared with existing CNN
type networks, which are often designed in heuristic manners without
theoretical guarantees, DPE is able to gain advantages from rich task cues
investigated on the bases of domain knowledges. Therefore, DPE actually
provides a generic ensemble methodology to integrate both knowledge and
data-based cues for different image enhancement tasks. More importantly, our
theoretical investigations verify that the feedforward propagations of DPE are
properly controlled toward our desired solution. Experimental results
demonstrate that the proposed DPE outperforms state-of-the-arts on a variety of
image enhancement tasks in terms of both quantitative measure and visual
perception quality.
