3 research outputs found
DELAD: Deep Landweber-guided deconvolution with Hessian and sparse prior
We present a model for non-blind image deconvolution that incorporates the
classic iterative method into a deep learning application. Instead of using
large over-parameterised generative networks to create sharp picture
representations, we build our network based on the iterative Landweber
deconvolution algorithm, which is integrated with trainable convolutional
layers to enhance the recovered image structures and details. Additional to the
data fidelity term, we also add Hessian and sparse constraints as
regularization terms to improve the image reconstruction quality. Our proposed
model is \textit{self-supervised} and converges to a solution based purely on
the input blurred image and respective blur kernel without the requirement of
any pre-training. We evaluate our technique using standard computer vision
benchmarking datasets as well as real microscope images obtained by our
enhanced depth-of-field (EDOF) underwater microscope, demonstrating the
capabilities of our model in a real-world application. The quantitative results
demonstrate that our approach is competitive with state-of-the-art non-blind
image deblurring methods despite having a fraction of the parameters and not
being pre-trained, demonstrating the efficiency and efficacy of embedding a
classic deconvolution approach inside a deep network.Comment: 9 pages, 7 figure
Leveraging Classic Deconvolution and Feature Extraction in Zero-Shot Image Restoration
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