Abstract: To solve the problem that X-ray with single energy could not simultaneously expose each part of a complex workpiece, a multi-energy digital radiography (DR) fusion network based on detail enhancement, namely dual-encoder nest connection-based fusion network, is proposed. In this network, the inception module is used as the basic convolution layer and a trainable LOG (Laplacian of Gaussian) convolution module is designed in the auxiliary branch of the dual-encoder to extract multi-scale edge features and add them to the main branch to enhance the global features. In the training stage, a local energy consistency loss function based on image block is proposed to reduce the local errors of input and output. In the fusion process, channel and spatial attention mechanisms are used as the fusion strategy to fuse the multi-scale enhanced features extracted from dual-encoder, and the fused multi-scale features are input into the nest connected decoder for reconstruction. Experimental results show that the proposed fusion network has the effect of detail enhancement and can reproduce the internal structure and defects of complex workpiece completely and clearly.