  In this paper we present TreEnhance, an automatic method for low-light image
enhancement capable of improving the quality of digital images. The method
combines tree search theory, and in particular the Monte Carlo Tree Search
(MCTS) algorithm, with deep reinforcement learning. Given as input a low-light
image, TreEnhance produces as output its enhanced version together with the
sequence of image editing operations used to obtain it. During the training
phase, the method repeatedly alternates two main phases: a generation phase,
where a modified version of MCTS explores the space of image editing operations
and selects the most promising sequence, and an optimization phase, where the
parameters of a neural network, implementing the enhancement policy, are
updated.
  Two different inference solutions are proposed for the enhancement of new
images: one is based on MCTS and is more accurate but more time and memory
consuming; the other directly applies the learned policy and is faster but
slightly less precise. As a further contribution, we propose a guided search
strategy that "reverses" the enhancement procedure that a photo editor applied
to a given input image. Unlike other methods from the state of the art,
TreEnhance does not pose any constraint on the image resolution and can be used
in a variety of scenarios with minimal tuning. We tested the method on two
datasets: the Low-Light dataset and the Adobe Five-K dataset obtaining good
results from both a qualitative and a quantitative point of view.
