We are a research group in UT Austin's Geometry of Space stream studying deep learning/computer vision applications in adaptive optics. This is a repository to store our research references.
- Adaptive optics based on machine learning: a review [Paper] [Notes]
- Galaxy Deblending using Residual Dense Neural network [Paper] [Notes]
- Deep learning for a space-variant deconvolution in galaxy surveys [Paper] [Notes]
- Deconvolution of Astronomical Images with Deep Neural Networks [Paper] [Notes]
- Deblending galaxies with variational autoencoders: A joint multiband, multi-instrument approach [Paper] [Notes]
- Analyzing and Processing of Astronomical Images using Deep Learning Techniques [Paper] [Notes]
- Blind Image Deblurring with Local Maximum Gradient Prior [Paper] [Notes]
- Deblending and classifying astronomical sources with Mask R-CNN deep learning [Paper] [Notes]
- Gaussian Process Classification for Galaxy Blend Identification in LSST [Paper] [Notes]
- Deblending galaxy superpositions with branched generative adversarial networks [Paper] [Notes]
- Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal [Paper] [Notes]
- Partial-Attribution Instance Segmentation for Astronomical Source Detection and Deblending [Paper] [Notes]