Non-blind deconvolution aims to restore a sharp image from its blurred counterpart given an obtained kernel. Existing deep neural architectures are often built based on large datasets of sharp ground truth images and trained with supervision. Sharp, high quality ground truth images, however, are not always available, especially for biomedical applications. This severely hampers the applicability of current approaches in practice. In this paper, we propose a novel non-blind deconvolution method that leverages the power of deep learning and classic iterative deconvolution algorithms. Our approach combines a pre-trained network to extract deep features from the input image with iterative Richardson-Lucy deconvolution steps. Subsequently, a zero-shot optimisation process is employed to integrate the deconvolved features, resulting in a high-quality reconstructed image. By performing the preliminary reconstruction with the classic iterative deconvolution method, we can effectively utilise a smaller network to produce the final image, thus accelerating the reconstruction whilst reducing the demand for valuable computational resources. Our method demonstrates significant improvements in various real-world applications non-blind deconvolution tasks.

翻译：非盲去卷积旨在根据已知的模糊核从模糊图像中恢复出清晰图像。现有的深度神经网络架构通常基于大量清晰真实图像数据集构建，并通过监督方式进行训练。然而，高质量的清晰真实图像并非总是可获取的，尤其是在生物医学应用中。这严重限制了当前方法在实际中的适用性。本文提出一种新颖的非盲去卷积方法，该方法融合了深度学习的优势与经典迭代去卷积算法。我们的方法将预训练网络用于从输入图像中提取深层特征，并与迭代Richardson-Lucy去卷积步骤相结合。随后，通过零样本优化过程整合去卷积后的特征，从而生成高质量的重建图像。通过采用经典迭代去卷积方法进行初步重建，我们能够有效利用较小的网络生成最终图像，从而加速重建过程并减少对宝贵计算资源的需求。该方法在多种实际非盲去卷积任务中展现了显著改进。