In addressing one of the most fundamental challenges in coherent imaging systems—the presence of speckle noise—this talk delves into both theoretical and empirical aspects of coherent image acquisition. Our theoretical framework is based on the deep image prior hypothesis that posits the existence of a convolutional neural network with iid noise as input, capable of generating natural images with properly tuned parameters. Our theoretical results reveal that acquiring high-quality images in such systems requires more measurements compared to those with additive noises. Furthermore, they reveal the advantages and bottlenecks of multilooking, a mechanism of capturing multiple images of the same scene. On the applied side, we introduce the "Bagged-DIP" approach, leveraging the DIP-hypothesis to enhance the performance of standard DIPs in image recovery from speckle-corrupted measurements.