Research across a myriad of science domains is increasingly reliant on image-based data from experiments. The challenge is to analyze the data torrent generated by these experiments in a timely manner and provide insights such as measurements for decision-making. Our goal is to construct software tools that help scientists uncover relevant, but hidden, information in digital images. In collaboration with colleagues at DOE-BES/ASCR and UCB-BIDS, we have exploited the scientific value of a broad array of high resolution, multidimensional datasets. This multi-disciplinary work is designed around a coordinated research effort connecting (1) state-of-the-art data analysis methods with basis on pattern recognition and machine learning; (2) emerging algorithms for dealing with massive datasets; and (3) advances in evolving computer architectures. These advances will accelerate the analyses of image-based recordings, scaling scientific procedures by reducing time between experiments, increasing efficiency, and opening more opportunities for more users of the imaging facilities.