| Summary: | Reflection removal is a crucial yet challenging task in computer vision, essential for enhancing image clarity and visibility in applications such as autonomous navigation, medical imaging diagnostics, and digital photography. However, existing methods often struggle with complex reflections and underutilize the rich information embedded in polarized images. To address these challenges, we propose PolAttNet, an innovative deep learning framework specifically designed for effective reflection removal in polarized images. PolAttNet integrates a novel combination of standard attention mechanisms and linear attention mechanisms to capture both global and local features. This dual-attention strategy enables the model to dynamically focus on the most relevant features, significantly enhancing its ability to suppress reflections without compromising computational efficiency. Furthermore, PolAttNet employs ConvNeXt blocks, advanced convolutional units designed to enhance the extraction of fine-grained features from polarized data. These blocks, coupled with Group Normalization, ensure stable and efficient training even with small batches of complex polarized data. We conduct extensive experiments on a public polarized dataset to evaluate the performance of PolAttNet. Our results demonstrate that PolAttNet outperforms state-of-the-art methods across various metrics, including Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The model excels in separating reflection and transmission layers, providing clear and high-quality images crucial for downstream tasks. Additionally, our ablation studies highlight the significant contributions of each component within PolAttNet, validating the effectiveness of our architectural choices. In summary, PolAttNet significantly advances polarized image processing, offering an efficient and robust solution for reflection removal.
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