Blind Image Denoising and Inpainting Using Robust Hadamard Autoencoders
This addresses a practical issue for image processing and real-world applications like manufacturing, where data is often noisy and incomplete, though it builds incrementally on prior work.
The paper tackles the problem of performing image denoising and inpainting without access to clean training data, by extending Robust Deep Autoencoders to handle noisy, partially observed datasets, and demonstrates this on MNIST and CIFAR10.
In this paper, we demonstrate how deep autoencoders can be generalized to the case of inpainting and denoising, even when no clean training data is available. In particular, we show how neural networks can be trained to perform all of these tasks simultaneously. While, deep autoencoders implemented by way of neural networks have demonstrated potential for denoising and anomaly detection, standard autoencoders have the drawback that they require access to clean data for training. However, recent work in Robust Deep Autoencoders (RDAEs) shows how autoencoders can be trained to eliminate outliers and noise in a dataset without access to any clean training data. Inspired by this work, we extend RDAEs to the case where data are not only noisy and have outliers, but also only partially observed. Moreover, the dataset we train the neural network on has the properties that all entries have noise, some entries are corrupted by large mistakes, and many entries are not even known. Given such an algorithm, many standard tasks, such as denoising, image inpainting, and unobserved entry imputation can all be accomplished simultaneously within the same framework. Herein we demonstrate these techniques on standard machine learning tasks, such as image inpainting and denoising for the MNIST and CIFAR10 datasets. However, these approaches are not only applicable to image processing problems, but also have wide ranging impacts on datasets arising from real-world problems, such as manufacturing and network processing, where noisy, partially observed data naturally arise.