摘要
In order to solve the problem of the lack of ornamental value and research value of ancient mural paintings due to low resolution and fuzzy texture details,a super resolution(SR)method based on generative adduction network(GAN)was proposed.This method reconstructed the detail texture of mural image better.Firstly,in view of the insufficient utilization of shallow image features,information distillation blocks(IDB)were introduced to extract shallow image features and enhance the output results of the network behind.Secondly,residual dense blocks with residual scaling and feature fusion(RRDB-Fs)were used to extract deep image features,which removed the BN layer in the residual block that affected the quality of image generation,and improved the training speed of the network.Furthermore,local feature fusion and global feature fusion were applied in the generation network,and the features of different levels were merged together adaptively,so that the reconstructed image contained rich details.Finally,in calculating the perceptual loss,the brightness consistency between the reconstructed fresco and the original fresco was enhanced by using the features before activation,while avoiding artificial interference.The experimental results showed that the peak signal-to-noise ratio and structural similarity metrics were improved compared with other algorithms,with an improvement of 0.512 dB-3.016 dB in peak signal-to-noise ratio and 0.009-0.089 in structural similarity,and the proposed method had better visual effects.
针对古代壁画分辨率低、纹理细节模糊导致壁画观赏性不足和研究价值不高的问题,本文基于生成对抗网络(GAN)提出了一种能更好地重建细节纹理的壁画图像超分辨率方法。首先,针对浅层图像特征利用不充分,引入信息蒸馏块提取图像浅层特征,增强后面网络的输出结果。其次,用RRDB-Fs提取深层图像特征,去除了残差块中影响图像生成质量的BN层,提高了网络的训练速度。再者,引入局部特征融合和全局特征融合,将不同层次的特征自适应地融合在一起,使重建出的图像含有丰富的细节信息。最后,在计算感知损失时使用激活前的特征,增强重建壁画图像亮度和原壁画图像亮度的一致性,同时避免伪影的产生。实验结果表明,本文所述方法和其他算法相比,具有较好的视觉效果,且重建图像的峰值信噪比和结构相似性指标均获得提高:峰值信噪比提高了0.512 d B-3.016 d B,结构相似性提高了0.009-0.089。
