Zhang Yuqiang, Yang Huamin, Han Cheng, Zhang Chao, Xu Chao, Lu Shiyu
School of Computer Science and Technology, Changchun University of Science and Technology, Changchun, Jilin, China.
PLoS One. 2025 May 20;20(5):e0318812. doi: 10.1371/journal.pone.0318812. eCollection 2025.
Projector compensation on non-flat, textured surfaces represents a formidable challenge in computational imaging, with conventional convolution-based methods frequently encountering critical limitations, especially in image edge regions characterized by complex geometric transformations. To systematically address these persistent challenges, we introduce IDNet, an innovative framework distinguished by its multi-scale receptive feature extraction modules. Central to our approach are multi-scale deformable convolution modules that dynamically adapt to geometric distortions through intelligently flexible sampling positions and precise offset mechanisms, which significantly enhance processing capabilities in intricate distortion regions. By strategically integrating non-local attention mechanisms, IDNet comprehensively captures global contextual information, thereby substantially improving both geometric and photometric compensation accuracy. Our experimental validation demonstrates that the proposed method achieves comparable compensation performance to existing approaches, particularly in the most challenging and geometrically complex edge regions of projected images.
在非平面、有纹理的表面上进行投影仪补偿是计算成像领域一项艰巨的挑战，传统的基于卷积的方法经常遇到严重的局限性，尤其是在具有复杂几何变换的图像边缘区域。为了系统地应对这些长期存在的挑战，我们引入了IDNet，这是一个创新框架，其特点是具有多尺度感受野特征提取模块。我们方法的核心是多尺度可变形卷积模块，它通过智能灵活的采样位置和精确的偏移机制动态适应几何失真，显著增强了在复杂失真区域的处理能力。通过策略性地集成非局部注意力机制，IDNet全面捕捉全局上下文信息，从而大幅提高几何和光度补偿精度。我们的实验验证表明，所提出的方法与现有方法具有相当的补偿性能，特别是在投影图像中最具挑战性和几何复杂的边缘区域。
