Astronomical image reconstruction with convolutional neural networks. Flamary, R. In *2017 25th European Signal Processing Conference (EUSIPCO)*, pages 2468-2472, Aug, 2017.

Paper doi abstract bibtex

Paper doi abstract bibtex

State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. Solving this problem can be computationally intensive and usually leads to a quadratic or at least superlinear complexity w.r.t. the number of pixels in the image. We investigate in this work the use of convolutional neural networks for image reconstruction in astronomy. With neural networks, the computationally intensive tasks is the training step, but the prediction step has a fixed complexity per pixel, i.e. a linear complexity. Numerical experiments show that our approach is both computationally efficient and competitive with other state of the art methods in addition to being interpretable.

@InProceedings{8081654, author = {R. Flamary}, booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)}, title = {Astronomical image reconstruction with convolutional neural networks}, year = {2017}, pages = {2468-2472}, abstract = {State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. Solving this problem can be computationally intensive and usually leads to a quadratic or at least superlinear complexity w.r.t. the number of pixels in the image. We investigate in this work the use of convolutional neural networks for image reconstruction in astronomy. With neural networks, the computationally intensive tasks is the training step, but the prediction step has a fixed complexity per pixel, i.e. a linear complexity. Numerical experiments show that our approach is both computationally efficient and competitive with other state of the art methods in addition to being interpretable.}, keywords = {astronomical image processing;astronomical techniques;image reconstruction;neural nets;optimisation;astronomical image reconstruction;convolutional neural networks;regularized constrained optimization problem;computationally intensive tasks;superlinear complexity;Image reconstruction;Neural networks;Convolution;Complexity theory;Training;Optimization}, doi = {10.23919/EUSIPCO.2017.8081654}, issn = {2076-1465}, month = {Aug}, url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570340787.pdf}, }

Downloads: 0

{"_id":"3tu8bGhCPErTKMEgF","bibbaseid":"flamary-astronomicalimagereconstructionwithconvolutionalneuralnetworks-2017","downloads":0,"creationDate":"2018-03-29T05:22:37.814Z","title":"Astronomical image reconstruction with convolutional neural networks","author_short":["Flamary, R."],"year":2017,"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/Roznn/EUSIPCO/main/eusipco2017url.bib","bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["R."],"propositions":[],"lastnames":["Flamary"],"suffixes":[]}],"booktitle":"2017 25th European Signal Processing Conference (EUSIPCO)","title":"Astronomical image reconstruction with convolutional neural networks","year":"2017","pages":"2468-2472","abstract":"State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. Solving this problem can be computationally intensive and usually leads to a quadratic or at least superlinear complexity w.r.t. the number of pixels in the image. We investigate in this work the use of convolutional neural networks for image reconstruction in astronomy. With neural networks, the computationally intensive tasks is the training step, but the prediction step has a fixed complexity per pixel, i.e. a linear complexity. Numerical experiments show that our approach is both computationally efficient and competitive with other state of the art methods in addition to being interpretable.","keywords":"astronomical image processing;astronomical techniques;image reconstruction;neural nets;optimisation;astronomical image reconstruction;convolutional neural networks;regularized constrained optimization problem;computationally intensive tasks;superlinear complexity;Image reconstruction;Neural networks;Convolution;Complexity theory;Training;Optimization","doi":"10.23919/EUSIPCO.2017.8081654","issn":"2076-1465","month":"Aug","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570340787.pdf","bibtex":"@InProceedings{8081654,\n author = {R. Flamary},\n booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},\n title = {Astronomical image reconstruction with convolutional neural networks},\n year = {2017},\n pages = {2468-2472},\n abstract = {State of the art methods in astronomical image reconstruction rely on the resolution of a regularized or constrained optimization problem. Solving this problem can be computationally intensive and usually leads to a quadratic or at least superlinear complexity w.r.t. the number of pixels in the image. We investigate in this work the use of convolutional neural networks for image reconstruction in astronomy. With neural networks, the computationally intensive tasks is the training step, but the prediction step has a fixed complexity per pixel, i.e. a linear complexity. Numerical experiments show that our approach is both computationally efficient and competitive with other state of the art methods in addition to being interpretable.},\n keywords = {astronomical image processing;astronomical techniques;image reconstruction;neural nets;optimisation;astronomical image reconstruction;convolutional neural networks;regularized constrained optimization problem;computationally intensive tasks;superlinear complexity;Image reconstruction;Neural networks;Convolution;Complexity theory;Training;Optimization},\n doi = {10.23919/EUSIPCO.2017.8081654},\n issn = {2076-1465},\n month = {Aug},\n url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570340787.pdf},\n}\n\n","author_short":["Flamary, R."],"key":"8081654","id":"8081654","bibbaseid":"flamary-astronomicalimagereconstructionwithconvolutionalneuralnetworks-2017","role":"author","urls":{"Paper":"https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570340787.pdf"},"keyword":["astronomical image processing;astronomical techniques;image reconstruction;neural nets;optimisation;astronomical image reconstruction;convolutional neural networks;regularized constrained optimization problem;computationally intensive tasks;superlinear complexity;Image reconstruction;Neural networks;Convolution;Complexity theory;Training;Optimization"],"metadata":{"authorlinks":{}}},"search_terms":["astronomical","image","reconstruction","convolutional","neural","networks","flamary"],"keywords":["astronomical image processing;astronomical techniques;image reconstruction;neural nets;optimisation;astronomical image reconstruction;convolutional neural networks;regularized constrained optimization problem;computationally intensive tasks;superlinear complexity;image reconstruction;neural networks;convolution;complexity theory;training;optimization"],"authorIDs":[],"dataSources":["Hs65vgcBNDGRfdk65","2MNbFYjMYTD6z7ExY"]}