计算机科学 ›› 2023, Vol. 50 ›› Issue (4): 117-124.doi: 10.11896/jsjkx.211200215
王振彪, 覃亚丽, 王荣芳, 郑欢
WANG Zhenbiao, QIN Yali, WANG Rongfang, ZHENG Huan
摘要: 基于深度学习的图像压缩感知方法由于其具有强大的学习能力和快速的处理速度受到了广泛关注。随着卷积神经网络深度的增加,现有使用神经网络的图像重构方法未充分利用网络中的残差特征。为了解决这一问题,通过联合优化采样和逆重构过程,提出了一个基于残差特征聚合的图像压缩感知注意力神经网络框架。首先,构建了块压缩感知采样子网络和初始重构子网络,以自适应地学习测量矩阵并生成初步的重构图像。然后引入残差学习与空间注意力机制,构建残差特征聚合注意力重构子网络使残差特征更加集中于关键的空间内容,以进一步提高重构图像质量。实验结果表明,所提网络在重构时间相当的情况下优于现有的图像压缩感知重构算法,获得了更加优良的图像压缩感知重构质量。具体地,在采样率为0.10的情况下,使用11幅图像进行测试,与其他基于深度学习的方法相比,其平均峰值信噪比提高了0.34~6.18 dB。
中图分类号:
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