Unsupervised Learning Method for Encoder-Decoder-Based Image Restoration

Abstract

The restoration of a corrupted image is a challenge to computer vision and image processing. In hazy, underwater and medical images, the lack of paired images lead the state of the art to synthesize datasets. The Generative Adversarial Networks (GANs) are widely used in these cases. However, computational cost and training instability are current concerns. We present an unsupervised learning algorithm that does not requires paired dataset to train encoder-decoder-like neural network for image restoration. An encoder-decoder learn to represent its input data in a latent representation and reconstruct then in the output. During the training stage, our algorithm applies the encoder-decoder output image to a degradation block that reinforces its degradation. The degraded and input images are matched using a loss function. After the training process, we obtain a restored image from the decoder. We used ill-exposed images to evaluate and validate our algorithm.