11 papers
Maximum Likelihood Reconstruction for Multi-Look Digital Holography with Markov-Modeled Speckle Correlation
Xi Chen, Arian Maleki, Shirin Jalali
Multi-look acquisition is a widely used strategy for reducing speckle noise in coherent imaging systems such as digital holography. By acquiring multiple measurements, speckle can…
Deep Learning-Based Snow Depth Retrieval Using Sentinel-1 Repeat-Pass InSAR
Nayan Yadav, Shadi Oveisgharan, Shirin Jalali
Snow depth plays a central role in seasonal snowpack characterization and the terrestrial water cycle, yet remains challenging to estimate at high spatial resolution. Recent studie…
Shot-Aware Frame Sampling for Video Understanding
Mengyu Zhao, Di Fu, Yongyu Xie +4
Video frame sampling is essential for efficient long-video understanding with Vision-Language Models (VLMs), since dense inputs are costly and often exceed context limits. Yet when…
Monte Carlo Maximum Likelihood Reconstruction for Digital Holography with Speckle
Xi Chen, Arian Maleki, Shirin Jalali
In coherent imaging, speckle is statistically modeled as multiplicative noise, posing a fundamental challenge for image reconstruction. While maximum likelihood estimation (MLE) pr…
Snapshot Compressive Imaging under Saturation: Theory, Mask Design, and Reconstruction
Mengyu Zhao, Shirin Jalali
Snapshot compressive imaging (SCI) acquires high-dimensional data cubes, such as videos and hyperspectral images, by optically multiplexing multiple coded frames into a single two-…
Zero-shot Denoising via Neural Compression: Theoretical and algorithmic framework
Ali Zafari, Xi Chen, Shirin Jalali
Zero-shot denoising aims to denoise observations without access to training samples or clean reference images. This setting is particularly relevant in practical imaging scenarios…