Start date: 2025 × End date: 2025 × Subject: Image processing ×

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Now showing 1 - 3 of 3
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    Iterative Nonparametric Bayesian CP Decomposition for Hyperspectral Image Denoising
    (The Eurographics Association, 2025) Liu, Wei; Jiang, Kaiwen; Lai, Jinzhi; Zhang, Xuesong; Wang, Beibei; Wilkie, Alexander
    Hyperspectral image (HSI) denoising relies on exploiting the multiway correlations hidden in the clean signals to discriminate between the randomness of measurement noise. This paper proposes a self-supervised model that has a three-layer algorithmic hierarchy to iteratively quest for the tensor decomposition based representation of the underlying HSI. The outer layer takes advantage of the non-local similarity of HSI via a simple but effective k-means++ algorithm to explore the patch-level correlation and yields clusters of patches with similar tensor ranks. The middle and inner layers consist of a Bayesian Nonparametric tensor decomposition framework. The middle one employs a multiplicative Gamma process prior for the low rank tensor decomposition, and a Gaussian-Wishart prior for a more flexible exploration of the correlations among the latent factor matrices. The inner layer is responsible for the finer regression of the residual multiway correlations leaked from the upper two layers. Our scheme also provides a principled and automatic solution to several practical HSI denoising factors, such as the noise level, the model complexity and the regularization weights. Extensive experiments validate that our method outperforms state-of-the-art methods on a series of HSI denoising metrics.
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    From Optical Measurement to Visual Comfort Analysis: a Complete Simulation Workflow with Ocean™'s Glare Map Post-processing
    (The Eurographics Association, 2025) Bandeliuk, Oleksandra; Besse, Grégoire; Pierrard, Thomas; Berthier, Estelle; Wang, Beibei; Wilkie, Alexander
    Lighting critically influences public safety and visual comfort across environments. Discomfort glare, in particular, poses a major challenge. We here introduce Ocean™'s glare map, a fast, high-fidelity glare evaluation tool that computes key indices (UGR, DGP, GR) through post-processing of spectral global illumination simulations. Beyond whole-scene assessments, our glare map tool uniquely offers per-source glare ratings, enabling precise design optimization. Through three practical use cases, we demonstrate the effectiveness of our tool for operational design and show how changes in illumination and material properties directly affect glare, supporting safer and more efficient lighting designs.
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    A Divisive Normalization Brightness Model for Tone Mapping
    (The Eurographics Association, 2025) Ding, Julian; Shirley, Peter; Wang, Beibei; Wilkie, Alexander
    Tone mapping operators (TMOs) are essential in digital graphics, enabling the conversion of high-dynamic-range (HDR) scenes to the limited dynamic range reproducible by display devices, while simultaneously preserving the perceived qualities of the scene. An important aspect of perceived scene fidelity is brightness: the perceived luminance at every position in the scene. We introduce DINOS, a neurally inspired brightness model combining the multi-scale architecture of several historical models with a divisive normalization structure suggested by experimental results from recent studies on neural responses in the human visual pathway. We then evaluate the brightness perception predicted by DINOS against several well-known brightness illusions, as well as human preferences from an existing study which quantitatively ranks 14 popular TMOs. Finally, we propose BRONTO: a brightness-optimized TMO that directly leverages DINOS to perform locally varying exposure. We demonstrate BRONTO's efficacy on a variety of HDR scenes and compare its performance against several other contemporary TMOs.