D2UNet: Dual Decoder U-Net for Seismic Image Super-Resolution Reconstruction

Super-resolution reconstruction is an essential task of seismic inversion due to the low resolution and strong noise of field data. Popular deep networks derived from U-Net lack the ability to recover detailed edge features and weak signals. In this article, we propose a dual decoder U-Net (D2UNet) to explore both the detail and edge information of the data. The encoder inputs the low-resolution image and the edge image obtained through the Canny algorithm. Edge images can provide rich shape and boundary information, which is helpful to generate more accurate and high-quality data. The dual decoder consists of a main decoder for high-resolution recovery and an edge decoder for edge contour detection. These two decoders interact with a texture-warping module (TWM) with deformable convolution. TWM aims to distort realistic edge details to match the fidelity of low-resolution inputs, especially the location of edges and weak signals. The loss function is a combination of
$L_{1}$
loss and multiscale structural similarity loss (MS-SSIM) to ensure perception quality. Results on synthetic and field seismic images show that D2UNet not only improves the resolution of noisy seismic images, but also maintains the image fidelity.