Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs

• URL: http://arxiv.org/abs/2305.12170v1
• Date: Sat, 20 May 2023 11:18:38 GMT
• Title: Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs
• Authors: Mengze Xu, Jie Ma, Yuanyuan Zhu
• Abstract summary: We propose a novel blind SR framework based on conditional denoising diffusion probabilistic models (DDPM) In our work, we introduce conditional denoising diffusion probabilistic models (DDPM) from two aspects: kernel estimation progress and re-construction progress. We construct a DDPM-based reconstructor to learning the mapping from the LR images to HR images.
• Score: 6.2678394285548755
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• Latent Diffusion, Implicit Amplification: Efficient Continuous-Scale Super-Resolution for Remote Sensing Images [7.920423405957888]
  E$2$DiffSR achieves superior objective metrics and visual quality compared to the state-of-the-art SR methods. It reduces the inference time of diffusion-based SR methods to a level comparable to that of non-diffusion methods.
  arXiv  Detail & Related papers  (2024-10-30T09:14:13Z)
• BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution [52.47005445345593]
  BlindDiff is a DM-based blind SR method to tackle the blind degradation settings in SISR. BlindDiff seamlessly integrates the MAP-based optimization into DMs. Experiments on both synthetic and real-world datasets show that BlindDiff achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2024-03-15T11:21:34Z)
• Efficient Test-Time Adaptation for Super-Resolution with Second-Order Degradation and Reconstruction [62.955327005837475]
  Image super-resolution (SR) aims to learn a mapping from low-resolution (LR) to high-resolution (HR) using paired HR-LR training images. We present an efficient test-time adaptation framework for SR, named SRTTA, which is able to quickly adapt SR models to test domains with different/unknown degradation types.
  arXiv  Detail & Related papers  (2023-10-29T13:58:57Z)
• DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior [70.46245698746874]
  We present DiffBIR, a general restoration pipeline that could handle different blind image restoration tasks. DiffBIR decouples blind image restoration problem into two stages: 1) degradation removal: removing image-independent content; 2) information regeneration: generating the lost image content. In the first stage, we use restoration modules to remove degradations and obtain high-fidelity restored results. For the second stage, we propose IRControlNet that leverages the generative ability of latent diffusion models to generate realistic details.
  arXiv  Detail & Related papers  (2023-08-29T07:11:52Z)
• DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration [66.01846902242355]
  Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training. It is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. We propose Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image.
  arXiv  Detail & Related papers  (2023-03-13T06:05:18Z)
• Kernelized Back-Projection Networks for Blind Super Resolution [14.422263486129811]
  Super Resolution (SR) fails to super-resolve Low-Resolution (LR) images degraded by arbitrary degradations. This paper reveals that non-blind SR that is trained simply with various blur kernels exhibits comparable performance as those with the degradation model for blind SR.
  arXiv  Detail & Related papers  (2023-02-16T18:35:39Z)
• Kernel-aware Raw Burst Blind Super-Resolution [0.0]
  Burst super-resolution (SR) provides a possibility of restoring rich details from low-quality images. Existing non-blind networks usually lead to a severe performance drop in recovering high-resolution (HR) images. We introduce a kernel-aware deformable alignment module which can effectively align the raw images with consideration of the blurry priors.
  arXiv  Detail & Related papers  (2021-12-14T11:49:13Z)
• End-to-end Alternating Optimization for Blind Super Resolution [68.395664154041]
  Two-step solution involves two independently trained models, which may not be well compatible with each other. We adopt an alternating optimization algorithm, which can estimate the blur kernel and restore the SR image in a single model. Our model can largely outperform state-of-the-art methods and produce more visually favorable results at a much higher speed.
  arXiv  Detail & Related papers  (2021-05-14T15:05:30Z)
• Deep Variational Network Toward Blind Image Restoration [60.45350399661175]
  Blind image restoration is a common yet challenging problem in computer vision. We propose a novel blind image restoration method, aiming to integrate both the advantages of them. Experiments on two typical blind IR tasks, namely image denoising and super-resolution, demonstrate that the proposed method achieves superior performance over current state-of-the-arts.
  arXiv  Detail & Related papers  (2020-08-25T03:30:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.