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The recent application of deep learning (DL) to various tasks has seen theperformance of classical techniques surpassed by their DL-based counterparts.As a result, DL has equally seen application in the removal of noise fromimages. In particular, the use of deep feed-forward convolutional neuralnetworks (DnCNNs) has been investigated for denoising. It utilizes advances inDL techniques such as deep architecture, residual learning, and batchnormalization to achieve better denoising performance when compared with theother classical state-of-the-art denoising algorithms. However, its deeparchitecture resulted in a huge set of trainable parameters. Meta-optimizationis a training approach of enabling algorithms to learn to train themselves bythemselves. Training algorithms using meta-optimizers have been shown to enablealgorithms to achieve better performance when compared to the classicalgradient descent-based training approach. In this work, we investigate theapplication of the meta-optimization training approach to the DnCNN denoisingalgorithm to enhance its denoising capability. Our preliminary experiments onsimpler algorithms reveal the prospects of utilizing the meta-optimizationtraining approach towards the enhancement of the DnCNN denoising capability.

Meta-Optimization of Deep CNN for Image Denoising Using LSTM