Fraction-Order Total Variation Image Blind Restoration Based on Self-Similarity Features

© Copyright 2020 The Authors. To improve the artifacts of the restoration results restored by existing blind restoration method, an effective image blind restoration method using self-similarity as prior information is proposed for restoring the blurry images. Firstly, the fraction-order model is achieved by extending integer-order total variation, which is prone to reduce artifacts. Motivated by the fact that the introduction of prior information is beneficial to improve the restoration results, we found that natural images usually exhibit some texture features. Self-similarity is a popular texture features and well-defined in the statistics. Therefore, this texture feature is introduced as prior information for the restoration model and further improving the restoration results. Finally, the cost function is generated and solved by semi-quadratic regularization. Experiments on various natural images showed that the proposed method can improve the performance relative to other image blind restoration algorithms in terms of both subjective vision and objective evaluation. The subjective analysis revealed that the proposed algorithm resulted in improved translation and improved artifact appearance. The objective evaluation showed that the proposed algorithm showed the best evaluation values, including Structural Similarity and Peak Signal-to-noise ratio. The restoration results of various images reveal that the proposed method is practical and effective in image restoration.10.13039/501100003819-Natural Science Foundation of Hubei Province (Grant Number: 2019CFB233); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 61901059)

Coordinate-based neural representations for computational adaptive optics in widefield microscopy

Widefield microscopy is widely used for non-invasive imaging of biological structures at subcellular resolution. When applied to complex specimen, its image quality is degraded by sample-induced optical aberration. Adaptive optics can correct wavefront distortion and restore diffraction-limited resolution but require wavefront sensing and corrective devices, increasing system complexity and cost. Here, we describe a self-supervised machine learning algorithm, CoCoA, that performs joint wavefront estimation and three-dimensional structural information extraction from a single input 3D image stack without the need for external training dataset. We implemented CoCoA for widefield imaging of mouse brain tissues and validated its performance with direct-wavefront-sensing-based adaptive optics. Importantly, we systematically explored and quantitatively characterized the limiting factors of CoCoA's performance. Using CoCoA, we demonstrated the first in vivo widefield mouse brain imaging using machine-learning-based adaptive optics. Incorporating coordinate-based neural representations and a forward physics model, the self-supervised scheme of CoCoA should be applicable to microscopy modalities in general.Comment: 33 pages, 5 figure

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