Abstract
Image denoising under low signal-to-noise ratio (SNR) and non-Gaussian noise is still a challenging problem in image processing. In this study, the authors prose a kind of improved convolution neural network auto-encoders for image denoising. Different from other priors based methods, the denoising auto-encoders (DAEs) can learn end-to-end mappings from noisy images to the target ones. This study research statistical features of image residual between the restored images and target images. According to the maximum entropy principle, the training loss function of the ordinary DAEs was modified with residual statistics as the constraint condition, and an improved training algorithm was proposed based on augmented Lagrange function method. Thus, the quality of restored image can be improved through removing image information from residual more efficiently. Experiments show not only the denoising effects of improved DAEs is superior to the original mean-square-error loss function DAEs in both peak SNR and Riesz feature similarity metric indexes, but also has the ability to suppress the different types of noises with different levels through a single model.
Published Version
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