2024/02/23 by Xi Chen, Chen, Xi, Zhaoran Hou +7
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #Applications (stat.AP) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Image and Video Processing (eess.IV) #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.15635
openalex publication_date 2024/02/23 · openalex created_date 2024/02/28 · openalex updated_date 2026/08/01
We investigate both the theoretical and algorithmic aspects of likelihood-based methods for recovering a complex-valued signal from multiple sets of measurements, referred to as looks, affected by speckle (multiplicative) noise. Our theoretical contributions include establishing the first existing theoretical upper bound on the Mean Squared Error (MSE) of the maximum likelihood estimator under the deep image prior hypothesis. Our theoretical results capture the dependence of MSE upon the number of parameters in the deep image prior, the number of looks, the signal dimension, and the number of measurements per look. On the algorithmic side, we introduce the concept of bagged Deep Image Priors (Bagged-DIP) and integrate them with projected gradient descent. Furthermore, we show how employing Newton-Schulz algorithm for calculating matrix inverses within the iterations of PGD reduces the computational complexity of the algorithm. We will show that this method achieves the state-of-the-art performance.