Paper: Identity Enhanced Residual Image Denoising
We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules and residual on the residual architecture for image denoising.
Our network structure possesses three distinctive features that are important for the noise removal task.
Firstly, each unit employs identity mappings as the skip connections and receives pre-activated input to preserve the gradient magnitude propagated in both the forward and backward directions.
Secondly, by utilizing dilated kernels for the convolution layers in the residual branch, each neuron in the last convolution layer of each module can observe the full receptive field of the first layer.
Lastly, we employ the residual on the residual architecture to ease the propagation of the high-level information. Contrary to current state-of-the-art real denoising networks, we also present a straightforward and single-stage network for real image denoising.
The proposed network produces remarkably higher numerical accuracy and better visual image quality than the classical state-of-the-art and CNN algorithms when being evaluated on the three conventional benchmark and three real-world datasets.
Figure 1. Denoising results: In the first row, an image corrupted by the Gaussian noise with σ = 50 from the BSD68 dataset ([42]). In the second row, a sample image from RNI15 ([32]) real noisy dataset. Our results have the best PSNR score for synthetic images, and unlike other methods, it does not have over-smoothing or over-contrasting artifacts. Best viewed in color on a highresolution display.
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