  In the realm of Low-Light Image Enhancement (LLIE), existing research
primarily focuses on enhancing images globally. However, many applications
require local LLIE, where users are allowed to illuminate specific regions
using an input mask, such as creating a protagonist stage or spotlight effect.
However, this task has received limited attention currently. This paper aims to
systematically define the requirements of local LLIE and proposes a novel
strategy to convert current existing global LLIE methods into local versions.
The image space is divided into three regions: Masked Area A be enlightened to
achieve the desired lighting effects; Transition Area B is a smooth transition
from the enlightened area (Area A) to the unchanged region (Area C). To achieve
the task of local LLIE, we introduce Region-Aware Normalization for Local
Enhancement, dubbed as RANLEN. RANLEN uses a dynamically designed mask-based
normalization operation, which enhances an image in a spatially varying manner,
ensuring that the enhancement results are consistent with the requirements
specified by the input mask. Additionally, a set of region-aware loss terms is
formulated to facilitate the learning of the local LLIE framework. Our strategy
can be applied to existing global LLIE networks with varying structures.
Extensive experiments demonstrate that our approach can produce the desired
lighting effects compared to global LLIE, all the while offering controllable
local enhancement with various mask shapes.
