Improved stacked-denoising auto encoder for tide image denoising
Xiaowen Lv, Zhenyu Xiong, Yourong Chen, Ke Wang · 2023
Tide images collected by outdoor cameras are susceptible to complex environmental factors and seriously affect the accuracy of object recognition algorithm. Therefore, an improved Stacked-Denoising Auto Encoder (SDAE) tide image denoising algorithm in complex environment is proposed. In order to increase the number of network layers and find features faster, an SDAE based on convolutional network is proposed. Inside the network architecture of SDAE, layers including input layer, convolutional layer, pooling layer, full connection layer and output convolutional pooling layer are introduced to effectively extract image noise features and reduce network training parameters. The experimental results show that the improved algorithm can effectively remove single noises such as blur, illumination imbalance, rain and fog in complex environment, and the PSNR value and SSIM value of images of various noise types are improved by at least 13% and 33% respectively, and the SSIM value of the denoised rain images is close to 1.