Lattice Fusion Networks for Image Denoising

A novel method for feature fusion in convolutional neural networks is proposed in this work. Different feature fusion techniques are suggested to facilitate the flow of information and improve the training of deep neural networks. Some of these techniques as well as the proposed model can be considered a type of Directed Acyclic Graph (DAG) Network, where a layer can receive inputs from other layers and have outputs to other layers. In the proposed general framework of Lattice Fusion Network (LFN), feature maps of each convolutional layer are passed to other layers based on a lattice graph structure, where nodes are convolutional layers. To investigate the performance of the model, a specific design based on the general framework of LFN is implemented for image denoising. Results are compared with state of the art methods. The proposed model produced competitive results with far fewer learnable parameters, which shows the effectiveness of LFNs for training of deep neural networks

Image Denoising for Strong Gaussian Noises With Specialized CNNs for Different Frequency Components

In machine learning approach to image denoising a network is trained to recover a clean image from a noisy one. In this paper a novel structure is proposed based on training multiple specialized networks as opposed to existing structures that are base on a single network. The proposed model is an alternative for training a very deep network to avoid issues like vanishing or exploding gradient. By dividing a very deep network into two smaller networks the same number of learnable parameters will be available, but two smaller networks should be trained which are easier to train. Over smoothing and waxy artifacts are major problems with existing methods; because the network tries to keep the Mean Square Error (MSE) low for general structures and details, which leads to overlooking of details. This problem is more severe in the presence of strong noise. To reduce this problem, in the proposed structure, the image is decomposed into its low and high frequency components and each component is used to train a separate denoising convolutional neural network. One network is specialized to reconstruct the general structure of the image and the other one is specialized to reconstruct the details. Results of the proposed method show higher peak signal to noise ratio (PSNR), and structural similarity index (SSIM) compared to a popular state of the art denoising method in the presence of strong noises.