Exploring ensembles and uncertainty minimization in denoising networks
This work addresses uncertainty minimization for image denoising, an incremental improvement in computer vision.
The paper tackled the problem of epistemic uncertainty in neural networks for image denoising, which limits performance improvements, by developing ensemble-based solutions with attention modules for fusion, resulting in better performance over baseline pre-trained denoising networks.
The development of neural networks has greatly improved the performance in various computer vision tasks. In the filed of image denoising, convolutional neural network based methods such as DnCNN break through the limits of classical methods, achieving better quantitative results. However, the epistemic uncertainty existing in neural networks limits further improvements in their performance over denoising tasks. Therefore, we develop and study different solutions to minimize uncertainty and further improve the removal of noise. From the perspective of ensemble learning, we implement manipulations to noisy images from the point of view of spatial and frequency domains and then denoise them using pre-trained denoising networks. We propose a fusion model consisting of two attention modules, which focus on assigning the proper weights to pixels and channels. The experimental results show that our model achieves better performance on top of the baseline of regular pre-trained denoising networks.