Abstract
<p indent=0mm>The degradation of mixed degraded images is more serious than that of single degradation types, and it is difficult to restore them by precise modeling. The key to restore mixed degraded images is to study the end-to-end neural network algorithm. Although the existing operation-wise attention network (OWAN) algorithm has a certain performance improvement, its network is too complex, it runs slowly, the restored image lacks high-frequency details, and the overall effect also has room for improvement. To solve these problems, an adaptive restoration algorithm based on hierarchical feature fusion is proposed. The algorithm directly fuses the features of different receptive field branches to enhance the structure of the restored image. The attention mechanism is used to dynamically fuse the features of different hierarchies to increase the adaptability and reduce the redundancy of the model. In addition, combining the <italic>L</italic><sub>1</sub> loss and perception loss, the visual perception effect of the restored image is enhanced. Experimental results on DIV2K, BSD500 and other data sets show that the proposed algorithm is better than the OWAN algorithm in terms of quantitative analysis of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), as well as subjective visual quality.
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