A Novel Multi-Frame Color Images Super-Resolution Framework based on Deep Convolutional Neural Network
Zhe Li, Shu Li, Jianmin Wang, Hongyang Wang · 2016
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.