基于简单通道注意力机制的单图像超分辨率重建算法

Single image super-resolution reconstruction algorithm based on simple channel attention mechanism

现有的单图像超分辨率重建算法一般存在重建图像过于失真或将低分辨率图像噪点放大的问题,针对上述两个问题,提出一种基于简单通道注意力机制的生成对抗网络(SCAGAN)模型。采用随机高阶退化模型缓解重建图像过于失真的问题;加入简单通道注意力机制模块到残差密集块中作为模型的生成器网络模块,解决重建图像将低分辨率图像重建后噪点会放大的问题。实验数据表明,与现有的超分辨率算法相比,该算法有效降低了重建图像过于失真与将低分辨率图像噪点放大的问题,重建出的图像更加真实自然。

Existing single-image super-resolution reconstruction algorithms generally have the problem that the reconstructed image is too distorted or the noise of the low-resolution image is amplified.To solve the above two problems,a generative adversarial network(SCAGAN)model based on a simple channel attention mechanism was proposed.The random high-order degradation model was used to alleviate the problem of too much distortion of the reconstructed image.The generator network module in which a simple channel attention mechanism module was added to the residual dense block was used to solve the noise magni-fication problem of reconstructing the low-resolution image after reconstructing the image.Experimental data show that,compared with the existing super-resolution algorithm,this algorithm effectively reduces the problem of too much distortion of the reconstructed image and enlarges the noise of the low-resolution image,and the reconstructed image is more real and natural.