@inproceedings{SpectralDefocusCam,author={Foley, Christian and Markley, Eric and Yanny, Kyrollos and Waller, Laura and Monakhova, Kristina},booktitle={2025 IEEE International Conference on Computational Photography (ICCP)},title={Spectral DefocusCam: Super-Resolved Hyperspectral Imaging <br> Through Defocus},year={2025},volume={},number={},pages={1-12},keywords={Photography;Superresolution;Robot vision systems;Prototypes;Cameras;System-on-chip;Spatial resolution;Image reconstruction;Hyperspectral imaging;Lenses;Computational Photography;Hyperspectral Imaging},doi={10.1109/ICCP64821.2025.11143845}}
QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems
Cassandra Tong Ye, Shamus Li, Tyler King, and 1 more author
@article{ye2025qutcc,title={QUTCC: Quantile Uncertainty Training and Conformal Calibration for Imaging Inverse Problems},author={Ye, Cassandra Tong and Li, Shamus and King, Tyler and Monakhova, Kristina},journal={arXiv preprint arXiv:2507.14760},year={2025},}
Single-shot HDR using conventional image sensor shutter functions and optical randomization
Xiang Dai, Kyrollos Yanny, Kristina Monakhova, and 1 more author
High-dynamic-range (HDR) imaging is an essential technique for overcoming the dynamic range limits of image sensors. The classic method relies on multiple exposures, which slows capture time, resulting in motion artifacts when imaging dynamic scenes. Single-shot HDR imaging alleviates this issue by encoding HDR data in a single exposure, then computationally recovering it. Many established methods use strong image priors to recover improperly exposed detail; these approaches struggle with extended highlight regions. In this work, we demonstrate a novel single-shot HDR capture method that utilizes the global reset release (GRR) shutter mode commonly found in off-the-shelf sensors. GRR shutter mode applies a longer exposure time to rows closer to the bottom of the sensor. We use optics that relay a randomly permuted (shuffled) image onto the sensor, effectively creating spatially randomized exposures across the scene. The resulting exposure diversity allows us to recover HDR data by solving an optimization problem with a simple total variation image prior. In simulation, we demonstrate that our method outperforms other single-shot methods when many sensor pixels are saturated (10 (% ) or more), and is competitive at modest saturation (1 (% ) ). Finally, we demonstrate a physical lab prototype that uses an off-the-shelf random fiber bundle for the optical shuffling. The fiber bundle is coupled to a low-cost commercial sensor operating in GRR shutter mode. Our prototype achieves a dynamic range of up to 73dB using an 8-bit sensor with 48dB dynamic range.
@article{dai2025randomfiberbundle,author={Dai, Xiang and Yanny, Kyrollos and Monakhova, Kristina and Antipa, Nicholas},title={Single-shot HDR using conventional image sensor shutter functions and optical randomization},year={2025},publisher={Association for Computing Machinery},address={New York, NY, USA},issn={0730-0301},url={https://doi.org/10.1145/3748718},doi={10.1145/3748718},journal={ACM Trans. Graph.},month=jul,keywords={high dynamic range (HDR) imaging},}
Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy
Cassandra Tong Ye, Jiashu Han, Kunzan Liu, and 4 more authors
@article{ye2025uncertainty,title={Learned, uncertainty-driven adaptive acquisition for photon-efficient scanning microscopy},author={Ye, Cassandra Tong and Han, Jiashu and Liu, Kunzan and Angelopoulos, Anastasios and Griffith, Linda and Monakhova, Kristina and You, Sixian},journal={Optics Express},volume={33},number={6},pages={12269-12287},year={2025},publisher={Optica Publishing Group},}
System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring
Jiashu Han, Kunzan Liu, Keith B. Isaacson, and 3 more authors
@article{han2025system,title={System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring},author={Han, Jiashu and Liu, Kunzan and Isaacson, Keith B. and Monakhova, Kristina and G., Griffith Linda and You, Sixian},journal={Nature Communications},year={2025},publisher={Nature},}
2023
Roadmap on Deep Learning for Microscopy
Giovanni Volpe, Carolina Wählby, Lei Tian, and 8 more authors
@article{volpe2023roadmap,title={Roadmap on Deep Learning for Microscopy},author={Volpe, Giovanni and W{\"a}hlby, Carolina and Tian, Lei and Hecht, Michael and Yakimovich, Artur and Monakhova, Kristina and Waller, Laura and Sbalzarini, Ivo F and Metzler, Christopher A and Xie, Mingyang and others},journal={ArXiv},year={2023},publisher={arXiv},}
2022
Physics-Informed Machine Learning for Computational Imaging
@book{monakhova2022physics,title={Physics-Informed Machine Learning for Computational Imaging},author={Monakhova, Kristina},publisher={University of California, Berkeley},tppubtype={phdthesis},year={2022},date={2022-07-01},urldate={2022-07-01},number={UCB/EECS-2022-177},school={EECS Department, University of California, Berkeley},}
Dancing under the stars: video denoising in starlight
Kristina Monakhova, Stephan R Richter, Laura Waller, and 1 more author
In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jul 2022
@inproceedings{monakhova2022dancing,title={Dancing under the stars: video denoising in starlight},author={Monakhova, Kristina and Richter, Stephan R and Waller, Laura and Koltun, Vladlen},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},pages={16241--16251},year={2022},video={https://www.youtube.com/watch?v=eFvQs2j9RMw},}
Deep learning for fast spatially varying deconvolution
Kyrollos Yanny, Kristina Monakhova, Richard W Shuai, and 1 more author
@article{yanny2022deep,title={Deep learning for fast spatially varying deconvolution},author={Yanny, Kyrollos and Monakhova, Kristina and Shuai, Richard W and Waller, Laura},journal={Optica},volume={9},number={1},pages={96--99},year={2022},publisher={Optical Society of America},}
2021
Untrained networks for compressive lensless photography
Kristina Monakhova, Vi Tran, Grace Kuo, and 1 more author
@article{monakhova2021untrained,title={Untrained networks for compressive lensless photography},author={Monakhova, Kristina and Tran, Vi and Kuo, Grace and Waller, Laura},journal={Optics Express},volume={29},number={13},pages={20913--20929},year={2021},publisher={Optica Publishing Group},}
2020
Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array
Kristina Monakhova, Kyrollos Yanny, Neerja Aggarwal, and 1 more author
@article{monakhova2020spectral,title={Spectral DiffuserCam: lensless snapshot hyperspectral imaging with a spectral filter array},author={Monakhova, Kristina and Yanny, Kyrollos and Aggarwal, Neerja and Waller, Laura},journal={Optica},volume={7},number={10},pages={1298--1307},year={2020},publisher={Optica Publishing Group},group={journal},}
Miniscope3D: optimized single-shot miniature 3D fluorescence microscopy
Kyrollos Yanny, Nick Antipa, William Liberti, and 6 more authors
@article{yanny2020miniscope3d,title={Miniscope3D: optimized single-shot miniature 3D fluorescence microscopy},author={Yanny, Kyrollos and Antipa, Nick and Liberti, William and Dehaeck, Sam and Monakhova, Kristina and Liu, Fanglin Linda and Shen, Konlin and Ng, Ren and Waller, Laura},journal={Light: Science \& Applications},volume={9},number={1},pages={171},year={2020},publisher={Nature Publishing Group UK London},}
2019
Learned reconstructions for practical mask-based lensless imaging
Kristina Monakhova, Joshua Yurtsever, Grace Kuo, and 3 more authors
@article{monakhova2019learned,title={Learned reconstructions for practical mask-based lensless imaging},author={Monakhova, Kristina and Yurtsever, Joshua and Kuo, Grace and Antipa, Nick and Yanny, Kyrollos and Waller, Laura},journal={Optics express},volume={27},number={20},pages={28075--28090},year={2019},publisher={Optica Publishing Group},}
