Single-image reflection removal (SIRR) aims to restore the latent background layer from a reflection-contaminated image. Despite the promising progress achieved by deep learning-based methods, the roles of negative training samples and descriptive prompts for the reflection severity are underexplored in most existing deep SIRR approaches, limiting their reflection removal performance and generalization capability. In this work, we introduce a novel training framework that synergistically leverages learnable prompts and image data to optimize the restoration network. To this end, we define reflection levels corresponding to varying degrees of reflection interference on the background content and learn reflection-level prompts to supervise the SIRR process. We propose an Iterative Reflection Level Reduction (IRLR) framework composed of a Restoration Network Training Module (RNTM) and a Reflection Level Learning Module (RLLM). Specifically, RNTM predicts the background layer under the guidance of prompts learned by RLLM, while RLLM in turn refines these prompts using outputs from RNTM. The two modules are trained iteratively to progressively reduce the reflection levels of estimated background layers. To initialize the prompts, we construct a dedicated reflection-level dataset for pretraining. For adaptively supervising RNTM, we design a new reflection-level-aware strategy to address the challenge of directly aligning the output background with the minimal reflection level. Comprehensive experimental results demonstrate that the proposed method significantly outperforms state-of-the-art methods on average performance across several released datasets, improving PSNR by 0.82 dB and SSIM by 0.0120, respectively. The source code and dataset are available at https://github.com/NamecantbeNULL/IRLR SIRR.
Song et al. (Thu,) studied this question.