Abstract:
Remote sensing (RS) dehazing is a challenge topic, as images captured under hazy scenarios often suffer from seriously quality degradation and inconsistency. RS image res...Show MoreMetadata
Abstract:
Remote sensing (RS) dehazing is a challenge topic, as images captured under hazy scenarios often suffer from seriously quality degradation and inconsistency. RS image restoration has been significantly improved with the use of learning-based ways, while current methods are still struggling to restore the complex details for large irregular RS images with ununiform haze. In this letter, we propose an adaptive diffusion null-space dehazing network (ADND-Net), which is a novel diffusion-model-based null-space (NULL) learning toward free-form RS image dehazing. Specifically, a range–null-space decomposition is applied to improve the reverse diffusion process for image consistence. With the help of range–null-space content, we further advance the adaptive region-based diffusion (RD) module to address the unlimited-size RS images and increase the dehazed image quality. Extensive experiments show that our designed model outperforms other comparing dehazing methods on both synthetic and real-world RS datasets.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 21)
Funding Agency:
College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China
College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China
College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China
College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China
College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China
College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China
College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China
College of Electronic Information Engineering, Shenyang Aerospace University, Shenyang, China