Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic
  Models for Blind Super-Resolution Reconstruction in RSIs
Mengze Xu Jie Ma Yuanyuan Zhu
Previous super-resolution reconstruction (SR) works are always designed on
the assumption that the degradation operation is fixed, such as bicubic
downsampling. However, as for remote sensing images, some unexpected factors
can cause the blurred visual performance, like weather factors, orbit altitude,
etc. Blind SR methods are proposed to deal with various degradations. There are
two main challenges of blind SR in RSIs: 1) the accu-rate estimation of
degradation kernels; 2) the realistic image generation in the ill-posed
problem. To rise to the challenge, we propose a novel blind SR framework based
on dual conditional denoising diffusion probabilistic models (DDSR). In our
work, we introduce conditional denoising diffusion probabilistic models (DDPM)
from two aspects: kernel estimation progress and re-construction progress,
named as the dual-diffusion. As for kernel estimation progress, conditioned on
low-resolution (LR) images, a new DDPM-based kernel predictor is constructed by
studying the invertible mapping between the kernel distribution and the latent
distribution. As for reconstruction progress, regarding the predicted
degradation kernels and LR images as conditional information, we construct a
DDPM-based reconstructor to learning the mapping from the LR images to HR
images. Com-prehensive experiments show the priority of our proposal com-pared
with SOTA blind SR methods. Source Code is available at
https://github.com/Lincoln20030413/DDSR