Basit O. Alawode

Basit O. Alawode, Motaz Alfarraj

The recent application of deep learning (DL) to various tasks has seen the performance of classical techniques surpassed by their DL-based counterparts. As a result, DL has equally seen application in the removal of noise from images. In particular, the use of deep feed-forward convolutional neural networks (DnCNNs) has been investigated for denoising. It utilizes advances in DL techniques such as deep architecture, residual learning, and batch normalization to achieve better denoising performance when compared with the other classical state-of-the-art denoising algorithms. However, its deep architecture resulted in a huge set of trainable parameters. Meta-optimization is a training approach of enabling algorithms to learn to train themselves by themselves. Training algorithms using meta-optimizers have been shown to enable algorithms to achieve better performance when compared to the classical gradient descent-based training approach. In this work, we investigate the application of the meta-optimization training approach to the DnCNN denoising algorithm to enhance its denoising capability. Our preliminary experiments on simpler algorithms reveal the prospects of utilizing the meta-optimization training approach towards the enhancement of the DnCNN denoising capability.

Basit O. Alawode, Mudassir Masood, Tarig Ballal et al.

In spite of the improvements achieved by the several denoising algorithms over the years, many of them still fail at preserving the fine details of the image after denoising. This is as a result of the smooth-out effect they have on the images. Most neural network-based algorithms have achieved better quantitative performance than the classical denoising algorithms. However, they also suffer from qualitative (visual) performance as a result of the smooth-out effect. In this paper, we propose an algorithm to address this shortcoming. We propose a deep collaborative filtering-based (Deep-CoFiB) algorithm for image denoising. This algorithm performs collaborative denoising of image patches in the sparse domain using a set of optimized neural network models. This results in a fast algorithm that is able to excellently obtain a trade-off between noise removal and details preservation. Extensive experiments show that the DeepCoFiB performed quantitatively (in terms of PSNR and SSIM) and qualitatively (visually) better than many of the state-of-the-art denoising algorithms.

Basit O. Alawode, Mudassir Masood

Recently, deep learning methods such as the convolutional neural networks have gained prominence in the area of image denoising. This is owing to their proven ability to surpass state-of-the-art classical image denoising algorithms such as block-matching and 3D filtering algorithm. Deep denoising convolutional neural networks use many feed-forward convolution layers with added regularization methods of batch normalization and residual learning to speed up training and improve denoising performance significantly. However, this comes at the expense of a huge number of trainable parameters. In this paper, we show that by employing an enhanced dense-sparse-dense network training procedure to the deep denoising convolutional neural networks, comparable denoising performance level can be achieved at a significantly reduced number of trainable parameters. We derive motivation from the fact that networks trained using the dense-sparse-dense approach have been shown to attain performance boost with reduced number of parameters. The proposed reduced deep denoising convolutional neural networks network is an efficient denoising model with significantly reduced parameters and comparable performance to the deep denoising convolutional neural networks. Additionally, denoising was achieved at significantly reduced processing time.