General Model-Agnostic Transfer Learning for Natural Degradation Image Enhancement

Abstract

In order to obtain high-quality images in natural conditions, natural degradation image enhancement has been a research hotspot in recent years. In this paper, we present a transfer learning approach for multiple types of natural degradation image enhancement. We propose to create a common source domain for various natural degradations and perform the transfer learning for every specific natural degradation individually. By reusing the general enhancement model, we can circumvent the scarcity of training dataset and the computation-intensive training process for deep learning methods. In the experiment, we transfer the general model to three target tasks: raining image enhancement, snowing image enhancement and underwater image enhancement. With the finetuning of only 5 epochs, the enhancement models have been able to outperform several state-of-the-art methods that designed for specific task.