用于图像反卷积的点扩散函数可逆性评估学习
Learning Point Spread Function Invertibility Assessment for Image Deconvolution
AI总结：
提出一种基于神经网络非线性映射的PSF可逆性评估度量，作为有效正则化提升图像反卷积质量，并应用于E2E优化设计衍射光学元件，性能超越基线框架。
摘要：
基于深度学习（DL）的图像反卷积（ID）展现出卓越的恢复性能，超越了传统线性方法。然而，依赖点扩散函数（PSF）解析特性（如特定频谱属性或卷积矩阵中的小条件数）以实现高恢复性能的传统ID方法不同，DL技术缺乏量化指标来评估PSF对DL辅助恢复的适用性。旨在提升反卷积质量，我们提出一种度量方法，采用非线性途径通过神经网络将任意PSF映射为单位脉冲来学习其可逆性。映射后的PSF与单位脉冲间的差异越小，表明DL网络成功实现反卷积的可能性越高。我们的研究发现，该度量与DL及传统方法的高恢复性能相关，从而可作为反卷积任务中的有效正则化项。该方法降低了传统条件数评估的计算复杂度，且为可微过程。这些特性使其能应用于通过端到端（E2E）优化设计衍射光学元件，实现可逆PSF，并优于E2E基线框架。
英文摘要：
Deep-learning (DL)-based image deconvolution (ID) has exhibited remarkable recovery performance, surpassing traditional linear methods. However, unlike traditional ID approaches that rely on analytical properties of the point spread function (PSF) to achieve high recovery performance - such as specific spectrum properties or small conditional numbers in the convolution matrix - DL techniques lack quantifiable metrics for evaluating PSF suitability for DL-assisted recovery. Aiming to enhance deconvolution quality, we propose a metric that employs a non-linear approach to learn the invertibility of an arbitrary PSF using a neural network by mapping it to a unit impulse. A lower discrepancy between the mapped PSF and a unit impulse indicates a higher likelihood of successful inversion by a DL network. Our findings reveal that this metric correlates with high recovery performance in DL and traditional methods, thereby serving as an effective regularizer in deconvolution tasks. This approach reduces the computational complexity over conventional condition number assessments and is a differentiable process. These useful properties allow its application in designing diffractive optical elements through end-to-end (E2E) optimization, achieving invertible PSFs, and outperforming the E2E baseline framework.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
