Methods of removing unwanted objects from aerial photography images using iterative approach

Removing objects from images refers both to the tasks of improving the quality of the image, for example, in the field of recovering damaged photographs, and the tasks of increasing safety when removing people or cars from aerial photography images with remote sensing of the earth. At the same time, methods for removing unwanted objects usually include two stages: selecting objects for removal and restoring texture in areas of the image. The first stage can be performed manually by users, if it is necessary to select specific objects, or automatically by training the model on different classes of objects. The problem of image restoration in the course of research was solved by various methods, the main one of which involves using of the values of neighboring pixels for rendering in distant areas. In recent years, methods using deep learning based on convolutional and generative neural networks have shown good results. The aim of the work is to develop a method for removing objects from aerial photography images with manually selecting objects and drawing textures in the processed area. The paper reviews modern methods of image restoration, among which the most promising are the use of deep learning networks, as well as texture analysis in the restored area. The proposed algorithm is based on an iterative approach when analyzing neighboring areas and gradually painting the restored area with a texture from neighboring pixels, taking into account the weight and contours of the boundaries. The article evaluates the effectiveness of the proposed method using the base of video sequences obtained from quadcopters and containing people and natural objects.
At the same time, both an expert assessment was carried out, which showed good visual results, and a comparison of the quality of the algorithm with known approaches according to the PSNR metric, which showed the best results in the presence of a complex texture in the scene.