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Aiming at the problems of blurred edge structure, loss of texture details, distortion and slow operation speed in medical image fusion, this paper proposes a medical image fusion model based on residual network. The network mainly consists of an encoder, fuser and decoder. A feature extraction module MSDN consisting of residual attention mechanism and dense blocks is designed in the encoder for extracting multi-scale deep features of the source image. A learnable fusion network is used in the fuser to replace the manually designed fusion rules, eliminating the adverse effects of manually designed fusion strategies on the fusion effect. The decoder obtains the fused image by layer-bylayer decoding and up-sampling. We use a two-stage training strategy to train the fusion model; in the first stage, the image reconstruction task is used to drive the training of the encoder-decoder; in the second stage, the trained encoder-decoder is fixed, and the residual fusion network is trained using an appropriate loss function. The experimental results show that the subjective visual effect of the fused image contains rich texture details and color information, and the comprehensive performance of the objective evaluation index is better than that of the comparison algorithm.
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