Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation

• URL: http://arxiv.org/abs/2308.03348v1
• Date: Mon, 7 Aug 2023 07:02:42 GMT
• Title: Cooperative Colorization: Exploring Latent Cross-Domain Priors for NIR Image Spectrum Translation
• Authors: Xingxing Yang, Jie Chen, Zaifeng Yang
• Abstract summary: Near-infrared (NIR) image spectrum translation is a challenging problem with many promising applications. We propose a cooperative learning paradigm that colorizes NIR images in parallel with another proxy grayscale colorization task. Experiments show that our proposed cooperative learning framework produces satisfactory spectrum translation outputs with diverse colors and rich textures.
• Score: 5.28882362783108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Near-infrared (NIR) image spectrum translation is a challenging problem with many promising applications. Existing methods struggle with the mapping ambiguity between the NIR and the RGB domains, and generalize poorly due to the limitations of models' learning capabilities and the unavailability of sufficient NIR-RGB image pairs for training. To address these challenges, we propose a cooperative learning paradigm that colorizes NIR images in parallel with another proxy grayscale colorization task by exploring latent cross-domain priors (i.e., latent spectrum context priors and task domain priors), dubbed CoColor. The complementary statistical and semantic spectrum information from these two task domains -- in the forms of pre-trained colorization networks -- are brought in as task domain priors. A bilateral domain translation module is subsequently designed, in which intermittent NIR images are generated from grayscale and colorized in parallel with authentic NIR images; and vice versa for the grayscale images. These intermittent transformations act as latent spectrum context priors for efficient domain knowledge exchange. We progressively fine-tune and fuse these modules with a series of pixel-level and feature-level consistency constraints. Experiments show that our proposed cooperative learning framework produces satisfactory spectrum translation outputs with diverse colors and rich textures, and outperforms state-of-the-art counterparts by 3.95dB and 4.66dB in terms of PNSR for the NIR and grayscale colorization tasks, respectively.

Related papers

• Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement [71.13353154514418]
  Low-light image enhancement, particularly in cross-domain tasks such as mapping from the raw domain to the sRGB domain, remains a significant challenge. We present a novel Mamba scanning mechanism, called RAWMamba, to effectively handle raw images with different CFAs. We also present a Retinex Decomposition Module (RDM) grounded in Retinex prior, which decouples illumination from reflectance to facilitate more effective denoising and automatic non-linear exposure correction.
  arXiv  Detail & Related papers  (2024-09-11T06:12:03Z)
• FDCE-Net: Underwater Image Enhancement with Embedding Frequency and Dual Color Encoder [49.79611204954311]
  Underwater images often suffer from various issues such as low brightness, color shift, blurred details, and noise due to absorption light and scattering caused by water and suspended particles. Previous underwater image enhancement (UIE) methods have primarily focused on spatial domain enhancement, neglecting the frequency domain information inherent in the images.
  arXiv  Detail & Related papers  (2024-04-27T15:16:34Z)
• Multi-scale HSV Color Feature Embedding for High-fidelity NIR-to-RGB Spectrum Translation [5.596598303356484]
  Existing methods fail to reconcile the tension between maintaining texture detail fidelity and achieving diverse color variations. We propose a Multi-scale HSV Color Feature Embedding Network (MCFNet) that decomposes the mapping process into three sub-tasks. The proposed MCFNet demonstrates substantial performance gains over the NIR image colorization task.
  arXiv  Detail & Related papers  (2024-04-25T15:33:23Z)
• Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation [6.580484964018551]
  We introduce a domain translation module that translates NIR source images into the grayscale target domain. By incorporating a progressive training strategy, the statistical and semantic knowledge from both task domains are efficiently aligned. Experiments show that our MPFNet outperforms state-of-the-art counterparts by 2.55 dB in the NIR-to-RGB spectral domain translation task.
  arXiv  Detail & Related papers  (2023-12-26T13:07:45Z)
• DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation [68.43628183890007]
  We argue that domain gaps can also be caused by different foreground (nucleus)-background ratios. First, we introduce a re-coloring method that relieves dramatic image color variations between different domains. Second, we propose a new instance normalization method that is robust to the variation in the foreground-background ratios.
  arXiv  Detail & Related papers  (2023-09-01T01:01:13Z)
• Residual Spatial Fusion Network for RGB-Thermal Semantic Segmentation [19.41334573257174]
  Traditional methods mostly use RGB images which are heavily affected by lighting conditions, eg, darkness. Recent studies show thermal images are robust to the night scenario as a compensating modality for segmentation. This work proposes a Residual Spatial Fusion Network (RSFNet) for RGB-T semantic segmentation.
  arXiv  Detail & Related papers  (2023-06-17T14:28:08Z)
• Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registration [59.02821429555375]
  We present a robust cross-modality generation-registration paradigm for unsupervised misaligned infrared and visible image fusion. To better fuse the registered infrared images and visible images, we present a feature Interaction Fusion Module (IFM)
  arXiv  Detail & Related papers  (2022-05-24T07:51:57Z)
• TBNet:Two-Stream Boundary-aware Network for Generic Image Manipulation Localization [49.521622399483846]
  We propose a novel end-to-end two-stream boundary-aware network (abbreviated as TBNet) for generic image manipulation localization. The proposed TBNet can significantly outperform state-of-the-art generic image manipulation localization methods in terms of both MCC and F1.
  arXiv  Detail & Related papers  (2021-08-10T08:22:05Z)
• Attention-Guided NIR Image Colorization via Adaptive Fusion of Semantic and Texture Clues [6.437931036166344]
  Near infrared (NIR) imaging has been widely applied in low-light imaging scenarios. It is difficult for human and algorithms to perceive the real scene in the colorless NIR domain. We propose a novel Attention-based NIR image colorization framework via Adaptive Fusion of Semantic and Texture clues.
  arXiv  Detail & Related papers  (2021-07-20T03:00:51Z)
• SFANet: A Spectrum-aware Feature Augmentation Network for Visible-Infrared Person Re-Identification [12.566284647658053]
  We propose a novel spectrum-aware feature augementation network named SFANet for cross-modality matching problem. Learning with grayscale-spectrum images, our model can apparently reduce modality discrepancy and detect inner structure relations. In feature-level, we improve the conventional two-stream network through balancing the number of specific and sharable convolutional blocks.
  arXiv  Detail & Related papers  (2021-02-24T08:57:32Z)
• Self-Supervised Representation Learning for RGB-D Salient Object Detection [93.17479956795862]
  We use Self-Supervised Representation Learning to design two pretext tasks: the cross-modal auto-encoder and the depth-contour estimation. Our pretext tasks require only a few and un RGB-D datasets to perform pre-training, which make the network capture rich semantic contexts. For the inherent problem of cross-modal fusion in RGB-D SOD, we propose a multi-path fusion module.
  arXiv  Detail & Related papers  (2021-01-29T09:16:06Z)

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.