Residual Dense Convolutional Autoencoder for High Noise Image Denoising
In the field of high noise image denoising,traditional convolutional auto-encoders face challenges in extracting meaning-ful depth feature information,resulting in poor image reconstruction quality.To address this issue and improve the reconstruction quality of high noise images,this paper proposes a residual-density convolutional auto-encoder network model.The model firstly uses convolutional operations instead of pooling operations to improve the characterisation of high noise images.Moreover,a three-stage dense residual network structure is designed for effective image feature mining during the coding and decoding stages.Finally,an optimised loss function is designed to further improve the quality of the reconstructed images.Experimental results show that the denoising method presented in this paper is capable of reconstructing high quality images from high noise images while preserving more detailed feature information.It confirms the effectiveness of the algorithm in image denoising.The pro-posed method effectively addresses the challenge of denoising high noise images and has significant practical value.
Image denoisingConvolutional autoencoderResidual dense convolutionHigh noise imageOptimized loss function
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