计算机科学 ›› 2025, Vol. 52 ›› Issue (6): 239-246.doi: 10.11896/jsjkx.240300058
郭业才1,2, 胡晓伟1, 毛湘南1
GUO Yecai1,2, HU Xiaowei1, MAO Xiangnan1
摘要: 针对图像去噪特征提取单一化以及特征利用率低,不能生成更清晰图像的问题,提出了级联多尺度特征融合残差真实图像去噪网络。该网络双分支自适应密集残差块采用双路非对称扩张卷积扩展图像感受野,在水平尺度上选择性地提取丰富的纹理特征。在多尺度空间U-Net模块中,利用多尺度空间融合块增强网络对图像整体结构的学习能力,学习不同层次的信息,获取基于图像空间和上下文信息的多级特征。跳跃连接促进结构之间的参数共享,使不同尺度的特征充分融合,保证信息的完整性。最后,采用双残差学习构建出清晰的去噪图像。结果表明,该算法在真实噪声数据集(DND和SIDD)上的峰值信噪比分别为39.68 dB和39.50 dB,结构相似性分别为0.953和0.957,优于主流去噪算法。所提算法在增强去噪性能的同时,也保留了更详细的信息,使图像质量进一步提升。
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