计算机科学 ›› 2023, Vol. 50 ›› Issue (6A): 220600240-5.doi: 10.11896/jsjkx.220600240
喻九阳, 张德安, 戴耀南, 胡天豪, 夏文凤
YU Jiuyang, ZHANG Dean, DAI Yaonan, HU Tianhao, XIA Wenfeng
摘要: 针对现有图像超分辨模型存在特征提取能力弱、模型参数量较复杂等问题,提出了一种结构化混合注意力网络的图像超分辨率重建模型,该模型在提高图像超分辨率重建效果的同时降低了模型的参数量。首先,对编码器进行结构化处理,通过通道数量的不同来提取更多的图像特征。其次,对编码器的输出特性进行注意力网络混合重组,从而加强图像的特征特性。最后,采用残差方式将输入的浅层图像特征直接与强化特征相混合,降低网络的参数量。实验结果表明,在公共数据集及不同放大倍率的前提下,文中构建模型的PSNR值和SSIM值基本是最优的,且网络结构的参数量较低,较好地平衡了图像超分辨率重建过程中性能和参数复杂度间的关系。
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