关键词: 低分辨率, 超分辨率, 卷积层, 反卷积层

Abstract: An improved image super-resolution algorithm based on composite convolutional neural network which consists of convolution network and deconvolution network is proposed to overcome many problems, such as over fitting of mapping function and problem of gradient dispersion. Firstly, the paper proposes a combination of RReLus and Softplus functions to replace previous activation functions, avoiding over fitting problem. Secondly, the mini-batch gradient descent method with correction coefficients is introduced into the network to avoiding the problem of gradient dispersion. Thirdly, it uses the deconvolution network to reconstruct high resolution image. Finally, experiments prove that the improved network model which obtains effectively improves image sharpness and edge sharpening, and achieves significant improvement in subjective visual effect and objective evaluation index.

Key words: low resolution, super-resolution, convolution layer, deconvolution layer