Gravitational lensing data is frequently collected at low resolution due to instrumental limitations and observing conditions. Machine learning-based super-resolution techniques offer a method to enhance the resolution of these images, enabling more precise measurements of lensing effects and a better understanding of the matter distribution in the lensing system. This enhancement can significantly improve our knowledge of the distribution of mass within the lensing galaxy and its environment, as well as the properties of the background source being lensed. Traditional super-resolution techniques typically learn a mapping function from lower-resolution to higher-resolution samples. However, these methods are often constrained by their dependence on optimizing a fixed distance function, which can result in the loss of intricate details crucial for astrophysical analysis. In this work, we introduce DiffLense, a novel super-resolution pipeline based on a conditional diffusion model specifically designed to enhance the resolution of gravitational lensing images obtained from the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP). Our approach adopts a generative model, leveraging the detailed structural information present in Hubble Space Telescope (HST) counterparts. The diffusion model, trained to generate HST data, is conditioned on HSC data pre-processed with denoising techniques and thresholding to significantly reduce noise and background interference. This process leads to a more distinct and less overlapping conditional distribution during the model's training phase. We demonstrate that DiffLense outperforms existing state-of-the-art single-image super-resolution techniques, particularly in retaining the fine details necessary for astrophysical analyses.
Generating realistic spray details in liquid simulations remains computationally expensive. This paper proposes a data‐driven method to simulate high‐resolution sprays on low‐resolution grids by retrieving details with the most compatible details from a precomputed repository efficiently. We first employ a random forest‐based distance (RFD) to measure the similarity of liquid regions. In consideration of spatiotemporal relationships between one liquid region and its neighbors, we define a multinary label for RFD instead of the original binary one. Our improved RFD enables us to retrieve details that fit ground truth the best. To ensure temporal continuity of our result and to generate new details from existing ones, we formulate a series of forests with a training set from different time steps. Then, we synthesize results of each forest according to their distances. Finally, we put the synthesis result in correct positions to generate desired sprays motion. In our method, a state‐of‐the‐art cascade forest is employed for a higher accuracy. Several experiments with various grid resolutions validate our method both in visual effect and computational cost.
more » « less- NSF-PAR ID:
- 10453433
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Computer Animation and Virtual Worlds
- Volume:
- 30
- Issue:
- 3-4
- ISSN:
- 1546-4261
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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This article was corrected on 19 July 2022. See the end of the full text for details.
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