6 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…
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…
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…
Multilook Coherent Imaging: Theoretical Guarantees and Algorithms
Xi Chen, Soham Jana, Christopher A. Metzler +2
Multilook coherent imaging is a widely used technique in applications such as digital holography, ultrasound imaging, and synthetic aperture radar. A central challenge in these sys…
DeCompress: Denoising via Neural Compression
Ali Zafari, Xi Chen, Shirin Jalali
Learning-based denoising algorithms achieve state-of-the-art performance across various denoising tasks. However, training such models relies on access to large training datasets c…
Comprehensive Examination of Unrolled Networks for Solving Linear Inverse Problems
Eric Chen, Xi Chen, Arian Maleki +1
Unrolled networks have become prevalent in various computer vision and imaging tasks. Although they have demonstrated remarkable efficacy in solving specific computer vision and co…