Couple-Domain Strategy for UAV Imagery Super-Resolution
Qi Zhao · 2024
As an effective mini-aircraft, Unmanned Aerial Vehicles (UAV) is popularly increasing with the demand for diverse tasks, in which visual sensor raw resolution is toughly demanded under the limitation about the amount physical constraints such as flight altitude and load capacity. Its raw resolution of UAV images generally lack sufficient representation for some details of the target, especially when the low contrast situation increases the difficulty of target detection and recognition. In order to improve the raw UAV imagery resolution, this paper proposes a deep learning-based image restoration method that simultaneously performs super-resolution in the perspective of couple-domain, which consists of the hybrid modules of frequency-based and spatial-based. For frequency domain, low-frequency and high-frequency sub-bands are decomposed from Low-resolution (LR) with the help of wavelet way. It is able to explore the structure of low-frequency content and the details of high-frequency information, which improves the resolution of UAV image to retain the edge details. For spatial domain, complementary operations are adopted to process different sub-images blocks after the LR decomposition, which can provide the difficulty degrees about their separate parts restoration. This way adopts different restorations on various regions with appropriate network capacities which is able to obtain the mapping consistencies between the LR and high-resolution (HR). Final results prove that above couple-domain structural interaction does not only exploit the powerful feature mapping ability, but also leverages the prior of the observation mode.