Title: Extra Proximal-Gradient Inspired Non-local Network for Image Reconstruction
Abstract: Variational method and deep learning method are two mainstream powerful approaches to solve inverse problems in computer vision. To take advantages of advanced optimization algorithms and powerful representation ability of deep neural networks, we propose a novel deep convolutional neural network for image reconstruction. The architecture of this network is inspired by our proposed accelerated extra proximal gradient algorithm with the incorporation of two types of prior-exploiting operations. They are a non-local operation to exploit the inherent non-local self-similarity of the images, and a sparsity-promoting operation to learn the nonlinear transform, under which the solution is sparse. Our experimental results showed that the proposed CNN outperforms several state-of-the-art deep neural networks with similar or even less number of learnable parameters. It also incorporates a non-local operation to exploit the non-local self-similarity of the images and to learn the nonlinear transform, under which the solution is sparse. Our experimental results showed that our network outperforms several state-of-the-art deep networks with similar number or only slightly increased number of learnable parameter.
时间：6月12日（周三）， 上午9：00-10：30
地点：教三 338
Yunmei Chen教授，美国佛罗里达大学终身教授。陈韵梅教授主要致力于数学和图像科学这一交叉学科的研究。研究课题不仅包括图像分析中数学模型的建立与数值方法的发展，而且对其潜在的数学理论进行了进一步的探索。陈韵梅教授被公认为偏微分方程与图像处理领域内的世界级科学家，在国际上具有崇高的学术地位。
Abstract: Variational method and deep learning method are two mainstream powerful approaches to solve inverse problems in computer vision. To take advantages of advanced optimization algorithms and powerful representation ability of deep neural networks, we propose a novel deep convolutional neural network for image reconstruction. The architecture of this network is inspired by our proposed accelerated extra proximal gradient algorithm with the incorporation of two types of prior-exploiting operations. They are a non-local operation to exploit the inherent non-local self-similarity of the images, and a sparsity-promoting operation to learn the nonlinear transform, under which the solution is sparse. Our experimental results showed that the proposed CNN outperforms several state-of-the-art deep neural networks with similar or even less number of learnable parameters. It also incorporates a non-local operation to exploit the non-local self-similarity of the images and to learn the nonlinear transform, under which the solution is sparse. Our experimental results showed that our network outperforms several state-of-the-art deep networks with similar number or only slightly increased number of learnable parameter.
时间：6月12日（周三）， 上午9：00-10：30
地点：教三 338
Yunmei Chen教授，美国佛罗里达大学终身教授。陈韵梅教授主要致力于数学和图像科学这一交叉学科的研究。研究课题不仅包括图像分析中数学模型的建立与数值方法的发展，而且对其潜在的数学理论进行了进一步的探索。陈韵梅教授被公认为偏微分方程与图像处理领域内的世界级科学家，在国际上具有崇高的学术地位。
