  Single image reflection removal is inherently ambiguous, as both the
reflection and transmission components requiring separation may follow natural
image statistics. Existing methods attempt to address the issue by using
various types of low-level and physics-based cues as sources of reflection
signals. However, these cues are not universally applicable, since they are
only observable in specific capture scenarios. This leads to a significant
performance drop when test images do not align with their assumptions. In this
paper, we aim to explore a novel flexible interactive reflection removal
approach that leverages various forms of sparse human guidance, such as points
and bounding boxes, as auxiliary high-level prior to achieve robust reflection
removal. However, incorporating the raw user guidance naively into the existing
reflection removal network does not result in performance gains. To this end,
we innovatively transform raw user input into a unified form -- reflection
masks using an Interactive Segmentation Foundation Model. Such a design absorbs
the quintessence of the foundational segmentation model and flexible human
guidance, thereby mitigating the challenges of reflection separations.
Furthermore, to fully utilize user guidance and reduce user annotation costs,
we design a mask-guided reflection removal network, comprising our proposed
self-adaptive prompt block. This block adaptively incorporates user guidance as
anchors and refines transmission features via cross-attention mechanisms.
Extensive results on real-world images validate that our method demonstrates
state-of-the-art performance on various datasets with the help of flexible and
sparse user guidance. Our code and dataset will be publicly available here
https://github.com/ShawnChenn/FlexibleReflectionRemoval.
