Edge-Preserving Image Restoration Based on Pixel-by-Pixel Reinforcement Learning
High-intensity Gaussian noise tends to blur or destroy the details and structure of an image,resulting in the loss of edge information.Therefore,an edge-preserving image restoration algorithm based on pixel-by-pixel reinforcement learning is proposed.First,a pixel-wise agent is constructed for each pixel.The algorithm uses a side window averaging filter at the edge of the action space.All pixel layer agents share the parameters of the advantageous actor-critic algorithm;therefore,the model can output the state transition probability of all positions simultaneously and select the appropriate strategy for the state transition to restore the image.Second,coordinated attention is combined in the feature extraction sharing network to focus on the global information of all pixel positions between the feature channels,to retain the position embedding information.Subsequently,to alleviate the problem of sparse rewards,an auxiliary loss,designed based on graph Laplacian regularity,focuses on the local smoothing information of the image,punishing the local unsmooth area to encourage the pixel-layer agent to learn the correct strategy,so as to more effectively maintain the edge.The experimental results show that the Peak Signal-to-Noise Ratio(PSNR)of the proposed algorithm on the Middlebury2005 and MNIST datasets is 32.97 dB and 28.26 dB,respectively,which is 0.23 dB and 0.75 dB higher than those obtained by the Pixel-RL algorithm,respectively.The total number of parameters and training time decrease by 44.9％and 18.2％,respectively,effectively reducing the complexity of the model while maintaining the edges.
万方数据
维普
