DDLPS: Detail-Based Deep Laplacian Pansharpening for Hyperspectral Imagery. - Kaiyan Li 002; Weiying Xie; Qian Du 001; Yunsong Li 001 (IEEE Trans. Geosci. Remote. Sens., 2019)

Supervised learning-based ideal observer approximation for joint detection and estimation tasks. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Task-based performance evaluation of deep neural network-based image denoising. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2021)

Assessing the Impact of Deep Neural Network-Based Image Denoising on Binary Signal Detection Tasks. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2021)

A task-informed model training method for deep neural network-based image denoising. - Kaiyan Li 002; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2022)

Investigation of adversarial robust training for establishing interpretable CNN-based numerical observers. - Sourya Sengupta; Craig K. Abbey; Kaiyan Li 002; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2022)

A Hybrid Approach for Approximating the Ideal Observer for Joint Signal Detection and Estimation Tasks by Use of Supervised Learning and Markov-Chain Monte Carlo Methods. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (IEEE Trans. Medical Imaging, 2022)

Estimating task-based performance bounds for image reconstruction methods by use of learned-ideal observers. - Kaiyan Li 002; Weimin Zhou; Hua Li 003; Mark A. Anastasio (Image Perception, Observer Performance, and Technology Assessment, 2023)