Flare-Aware RWKV for Flare Removal

Lens flare artifacts often emerge when capturing images under light sources due to the reflection and scattering of light. While existing methods primarily focus on data synthesis and collection schemes, there is a lack of specific architecture designed for this task. In this paper, we propose a RWKV-based network architecture suitable for flare removal. Firstly, we introduce a lightweight flare detection network to guide subsequent flare removal processes. Subsequently, we present a restoration network based on RWKV that efficiently captures global dependencies with linear computational complexity. Furthermore, we analyze the significance of two key modules within RWKV for this task, i.e., the attention mechanism and the token shift mechanism. We carefully select and integrate these mechanisms with minor adjustments specifically tailored for flare removal purposes. Our method demonstrates favorable performance across different datasets, particularly on real-world scenarios.