PromptHSI: Universal Hyperspectral Image Restoration Framework for Composite Degradation

• URL: http://arxiv.org/abs/2411.15922v1
• Date: Sun, 24 Nov 2024 17:08:58 GMT
• Title: PromptHSI: Universal Hyperspectral Image Restoration Framework for Composite Degradation
• Authors: Chia-Ming Lee, Ching-Heng Cheng, Yu-Fan Lin, Yi-Ching Cheng, Wo-Ting Liao, Chih-Chung Hsu, Fu-En Yang, Yu-Chiang Frank Wang,
• Abstract summary: We propose PromptHSI, the first universal AiO HSI restoration framework. We decompose text prompts into intensity and bias controllers to guide the restoration process. Our unified architecture excels at both fine-grained recovery and global information restoration tasks.
• Score: 28.10512516485237
• License:
• Abstract: Recent developments in All-in-One (AiO) RGB image restoration and prompt learning have enabled the representation of distinct degradations through prompts, allowing degraded images to be effectively addressed by a single restoration model. However, this paradigm faces significant challenges when transferring to hyperspectral image (HSI) restoration tasks due to: 1) the domain gap between RGB and HSI features and difference on their structures, 2) information loss in visual prompts under severe composite degradations, and 3) difficulties in capturing HSI-specific degradation representations through text prompts. To address these challenges, we propose PromptHSI, the first universal AiO HSI restoration framework. By leveraging the frequency-aware feature modulation based on characteristics of HSI degradations, we decompose text prompts into intensity and bias controllers to effectively guide the restoration process while avoiding domain gaps. Our unified architecture excels at both fine-grained recovery and global information restoration tasks. Experimental results demonstrate superior performance under various degradation combinations, indicating great potential for practical remote sensing applications. The source code and dataset will be publicly released.

Related papers

• Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration [33.163161549726446]
  Perceive-IR is an all-in-one image restorer designed to achieve fine-grained quality control. In the prompt learning stage, we leverage prompt learning to acquire a fine-grained quality perceiver capable of distinguishing three-tier quality levels. For the restoration stage, a semantic guidance module and compact feature extraction are proposed to further promote the restoration process.
  arXiv  Detail & Related papers  (2024-08-28T17:58:54Z)
• Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration [19.87693298262894]
  We propose Diff-Restorer, a universal image restoration method based on the diffusion model. We utilize the pre-trained visual language model to extract visual prompts from degraded images. We also design a Degradation-aware Decoder to perform structural correction and convert the latent code to the pixel domain.
  arXiv  Detail & Related papers  (2024-07-04T05:01:10Z)
• HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models [38.74983301496911]
  Hyperspectral image (HSI) restoration aims at recovering clean images from degraded observations. Existing model-based methods have limitations in accurately modeling the complex image characteristics. This paper proposes an unsupervised HSI restoration framework with pre-trained diffusion model (HIR-Diff)
  arXiv  Detail & Related papers  (2024-02-24T17:15:05Z)
• SPIRE: Semantic Prompt-Driven Image Restoration [66.26165625929747]
  We develop SPIRE, a Semantic and restoration Prompt-driven Image Restoration framework. Our approach is the first framework that supports fine-level instruction through language-based quantitative specification of the restoration strength. Our experiments demonstrate the superior restoration performance of SPIRE compared to the state of the arts.
  arXiv  Detail & Related papers  (2023-12-18T17:02:30Z)
• Cross-Consistent Deep Unfolding Network for Adaptive All-In-One Video Restoration [78.14941737723501]
  We propose a Cross-consistent Deep Unfolding Network (CDUN) for All-In-One VR. By orchestrating two cascading procedures, CDUN achieves adaptive processing for diverse degradations. In addition, we introduce a window-based inter-frame fusion strategy to utilize information from more adjacent frames.
  arXiv  Detail & Related papers  (2023-09-04T14:18:00Z)
• 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)
• HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging [138.04956118993934]
  We propose a high-resolution dual-domain learning network (HDNet) for HSI reconstruction. On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features. On the other hand, frequency domain learning (FDL) is introduced for HSI reconstruction to narrow the frequency domain discrepancy.
  arXiv  Detail & Related papers  (2022-03-04T06:37:45Z)
• Implicit Subspace Prior Learning for Dual-Blind Face Restoration [66.67059961379923]
  A novel implicit subspace prior learning (ISPL) framework is proposed as a generic solution to dual-blind face restoration. Experimental results demonstrate significant perception-distortion improvement of ISPL against existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-10-12T08:04:24Z)
• HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment [63.333407973913374]
  "Face Renovation"(FR) is a semantic-guided generation problem. "HiFaceGAN" is a multi-stage framework containing several nested CSR units. experiments on both synthetic and real face images have verified the superior performance of HiFaceGAN.
  arXiv  Detail & Related papers  (2020-05-11T11:33:17Z)

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.