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A Novel Multi-Frame Color Images Super-Resolution Framework based on Deep Convolutional Neural Network

机译:一种基于深卷积神经网络的新型多帧彩色图像超分辨率框架

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摘要

With the extensive application of machine learning. Deep convolution neural network (DCNN) learning method is developed on the basis of a multi-layer neural network for image classification and identification of specially designed. It has been improved and applied for single image super-resolution problem and demonstrated state-of-the-art quality. In this paper, we presents a novel framework based on deep convolutional neural network to realize the multi-frame color images super-resolution. The system contains two parts, multi-frame Image pixel processing and structure design of DCNN. The prior information could be utilized during the image pixel processing. Experimental results prove its effectiveness and confirm out framework can be effectively applied to multi-frame color images super-resolution. The generated super-resolution image achieves a better restoration image quality compared to state-of-the-art methods.
机译:随着机器学习的广泛应用。深度卷积神经网络(DCNN)学习方法是在多层神经网络的基础上开发的,用于图像分类和专门设计的识别。它已经改进并应用了单图像超分辨率问题,并证明了最先进的质量。在本文中,我们提出了一种基于深度卷积神经网络的新颖框架,实现多帧彩色图像超分辨率。该系统包含两部分,多帧图像像素处理和DCNN的结构设计。可以在图像像素处理期间使用先前的信息。实验结果证明了其有效性,并确认框架可以有效地应用于多帧彩色图像超分辨率。与最先进的方法相比,所产生的超分辨率图像达到更好的恢复图像质量。

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