In the realm of AI, data serves as a pivotal resource. Real-world hyperspectral images (HSIs), bearing wide spectral characteristics, are particularly valuable. However, the acquisition of HSIs is always costly and time-intensive, resulting in a severe data-thirsty issue in HSI research and applications. Current solutions have not been able to generate a sufficient volume of diverse and reliable synthetic HSIs. To this end, our study formulates a novel, generalized paradigm for HSI synthesis, i.e., unmixing before fusion, that initiates with unmixing across multi-source data and follows by fusion-based synthesis. By integrating unmixing, this work maps unpaired HSI and RGB data to a low-dimensional abundance space, greatly alleviating the difficulty of generating high-dimensional samples. Moreover, incorporating abundances inferred from unpaired RGB images into generative models allows for cost-effective supplementation of various realistic spatial distributions in abundance synthesis. Our proposed paradigm can be instrumental with a series of deep generative models, filling a significant gap in the field and enabling the generation of vast high-quality HSI samples for large-scale downstream tasks. Extension experiments on downstream tasks demonstrate the effectiveness of synthesized HSIs. The code is available at HSI-Synthesis.github.io.

The scarcity of hyperspectral images (HSIs) hinders the development of processing methods and downstream applications. HSI synthesis, which aims to generate realistic samples from the existing datasets, is undoubtedly a prospective and economical solution for the HSI data shortage problem. Inspired by the impressive performance of the diffusion model (DM) in image synthesis tasks, this article initiatively proposes an unmixing diffusion (UnmixDiff) model for high-quality HSI generation. The method starts with training an unmixing network to learn the distribution characteristic of objects (abundance). By incorporating the unmixing autoencoder into the DM, the UnmixDiff transforms the HSI generation into the abundance domain, which maintains the consistency of the generated spectral profile, reduces the computational complexity, and introduces a clear physical interpretation into the hyperspectral image synthesis tasks. After that, we construct a diffusion generation model in abundance space to generate realistic abundance maps. Instead of synthesizing original hyperspectral images, the proposed UnmixDiff synthesizes abundance maps to simulate objects’ distribution rather than superficial textures. In this way, realistic HSI samples are generated by mixing the synthesized abundance with the scene end-members. With the comparative experiments, the proposed method achieves state-of-the-art performance in HSI synthesis tasks, effectively alleviating the HSI data scarcity and supporting widespread HSI applications. The code is available athttps://github.com/yuyang95/UnmixingDM.

Seeking feature correspondences among two or more images is an important problem in computer vision and image processing. The putative matches constructed by the similarity of feature descriptors are often contaminated by many false matches. Typically, the local neighborhood points of a true match point have a rank order, which will be maintained in the corresponding image, and we call it rank consistency. In this paper, we design a number of sorting plans to obtain the neighborhood rank lists by taking full advantage of the local neighborhood geometry structure. In order to measure the differences between rank lists, we adopt the statistically famous Kendall rank correlation coefficient and generalize its definition for matching problem. We design a neighborhood common element guidance strategy and a multi-neighborhood strategy to improve the universality and robustness of our method. Our method has linear complexity and it has superiority over state-of-the-art methods on several challenging data sets. It also performs well in image registration and loop-closure detection tasks. The source code of our method is publicly available athttps://github.com/MnYangs/mGKRCC.

For a natural scene with nonuniform environment light, the captured visible images are always under- or over-exposed because of the limited dynamic range of digital imaging devices. Multi-exposure image fusion (MEF) is a mainstream and effective solution. For a local region that has friendly visual effect in one exposure setting but extremely bad-exposed in another, most existing MEF methods have the ability to transfer the scene detail information to the fused images. However, they will be affected by the over-high or -low light inevitably thus resulting in local visibility reduction. To address this issue, we propose an adaptive clarity evaluation-guided network with illumination correction for MEF in a coarse-to-fine manner, which is termed as ACE-MEF. To be specific, our ACE-MEF is mainly composed of two modules: clarity preservation network (CPN) and illumination adjustment network (IAN). Based on the adaptive clarity evaluation, CPN could be trained to coarsely preserve the environment light and texture details of the clearer regions in source images. Therefore, the need for labeled reference images that are time-consuming to obtain could be mitigated. By measuring the parameter maps of gamma function, IAN is able to refine and correct the local bad-exposed regions so that more details could be further revealed. Extensive experiments demonstrate that our method outperforms multiple state-of-the-art algorithms qualitatively and quantitatively.