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Total Internal Reflection Fluorescence Microscopy01:05

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  3. Single-image Reflection Removal Via Iterative Prompt Learning Of Reflection Level.
  1. Home
  3. Single-image Reflection Removal Via Iterative Prompt Learning Of Reflection Level.

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Single-Image Reflection Removal via Iterative Prompt Learning of Reflection Level.

Binbin Song, Jiantao Zhou, Shuning Xu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |May 28, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    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.

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    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.