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Single-Image Reflection Removal via Iterative Prompt Learning of Reflection Level
Summary
This study introduces a new Iterative Reflection Level Reduction (IRLR) framework for single-image reflection removal (SIRR). The method uses learnable prompts and iterative training to significantly improve reflection removal performance and generalization.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Image Processing
Background:
- Single-image reflection removal (SIRR) is crucial for recovering underlying background layers from images contaminated by reflections.
- Existing deep learning methods for SIRR often overlook the impact of negative training samples and descriptive prompts for reflection severity, limiting performance and generalization.
- There is a need for advanced training frameworks that can effectively handle varying degrees of reflection interference.
Purpose of the Study:
- To develop a novel training framework that synergistically combines learnable prompts and image data for optimizing deep SIRR networks.
- To address the underexplored roles of negative training samples and reflection severity prompts in existing SIRR approaches.
- To enhance the performance and generalization capabilities of single-image reflection removal techniques.
Main Methods:
- Proposed an Iterative Reflection Level Reduction (IRLR) framework, comprising a Restoration Network Training Module (RNTM) and a Reflection Level Learning Module (RLLM).
- RNTM predicts the background layer guided by prompts from RLLM, while RLLM refines these prompts based on RNTM's outputs, enabling iterative reduction of reflection levels.
- Introduced a reflection-level-aware strategy for adaptive supervision and constructed a dedicated dataset for pretraining reflection-level prompts.
Main Results:
- The proposed IRLR framework significantly outperforms state-of-the-art methods in single-image reflection removal.
- Achieved average performance improvements of 0.82 dB in Peak Signal-to-Noise Ratio (PSNR) and 0.0120 in Structural Similarity Index Measure (SSIM) across multiple datasets.
- Demonstrated enhanced generalization capability compared to existing deep SIRR approaches.
Conclusions:
- The synergistic combination of learnable prompts and iterative training within the IRLR framework effectively addresses limitations in current SIRR methods.
- The proposed approach offers a significant advancement in restoring background layers from reflection-contaminated images.
- The method shows strong potential for practical applications requiring high-quality image restoration.
