Novel image restoration method based on multi-frame super-resolution for atmospherically distorted images

In this study, the authors propose a novel multi-frame super-resolution method using frame selection and multiple fusions for atmospherically distorted, zoomed-in, image-quality enhancement. When a small part of the image captured by placing a target several kilometres away from the fixed camera is enlarged, the quality of the part becomes poor owing to low resolution, spatial deformations and noise that are mainly caused by long distance and atmospheric turbulence. Thus, the authors propose an adaptive frame selection method that selects only a few frames with small blur based on the corresponding images with relatively clear edges. Further, they propose multiple fusion schemes to reconstruct the selected frames, thereby suppressing the influence of deformation. By converting all the frames into high-resolution based on each frame and integrating them, deformation and noise are effectively removed without high computation cost using the multiple fusion scheme. The proposed method, which enhances the quality of atmospherically distorted zoomed-in images, exhibits superior performance than the state-of-the-art image super-resolution methods with regard to high accuracy, efficiency and ease of implementation, ensuring that the proposed method is suitable for enhancing the quality of an image captured using a general digital camera or a smartphone.