To filter noises and preserve the details of seismic images, a denoising method based on kernel prediction convolution neural network (CNN) architecture is proposed. The method consists of two convolution layers and a residual connection, containing a source sensing encoder, a spatial feature extractor and a kernel predictor. The scalar kernel was normalized by the softmax function to obtain the denoised images. In addition, to avoid excessive blur at the expense of image details, the authors put forward the concept of asymmetric loss function, which would enable users to control the level of residual noise and make a trade-off between variance and deviation. The experimental results show the proposed method achieved good denoising effect. Compared with some other excellent methods, the proposed method increased the peak signal-to-noise ratio (PSNR) by about 1.0–3.2 dB for seismic images without discontinuity, and about 1.8–3.9 dB for seismic images with discontinuity.

中文翻译：

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一种基于核预测CNN架构的地震图像去噪方法

为了过滤噪声并保留地震图像的细节，提出了一种基于核预测卷积神经网络（CNN）架构的去噪方法。该方法由两个卷积层和一个残差连接组成，包含一个源感知编码器、一个空间特征提取器和一个内核预测器。标量核通过softmax函数归一化以获得去噪图像。此外，为了避免过度模糊以牺牲图像细节为代价，作者提出了非对称损失函数的概念，可以让用户控制残留噪声的水平，并在方差和偏差之间做出权衡。实验结果表明，该方法取得了良好的去噪效果。与其他一些优秀的方法相比，