Blind Restoration of Images Distorted by Atmospheric Turbulence Based on Deep Transfer Learning

Abstract

Removing space-time varying blur and geometric distortions simultaneously from an image is a challenging task. Recent methods (including physical-based methods or learning-based methods) commonly default the turbulence-degraded operator as a fixed convolution operator. Obviously, the assumption does not hold in practice. According to the situation that the real turbulence distorted operator has double uncertainty in space and time dimensions, this paper reports a novel deep transfer learning (DTL) network framework to address this problem. Concretely, the training process of the proposed approach contains two stages. In the first stage, the GoPro Dataset was used to pre-train the Network D1 and freeze the bottom weight parameters of the model; in the second stage, a small amount of the Hot-Air Dataset was employed for finetuning the last two layers of the network. Furthermore, residual fast Fourier transform with convolution block (Res FFT-Conv Block) was introduced to integrate both low-frequency and high-frequency residual information. Subsequently, extensive experiments were carried out with multiple real-world degraded datasets by implementing the proposed method and four existing state-of-the-art methods. In contrast, the proposed method demonstrates a significant improvement over the four reported methods in terms of alleviating the blur and distortions, as well as improving the visual quality.

Keywords

• blind restoration
• images distorted by atmospheric turbulence
• deep transfer learning
• Res FFT-Conv Block
• high-quality images