Motion analysis is a fundamental problem that serves as the basis for many other image analysis tasks, such as structure estimation and object segmentation. Many motion analysis techniques assume that objects are opaque and non-reflective, asserting that a single pixel is an observation of a single scene object. This assumption breaks down when observing semitransparent objects--a single pixel is an observation of the object and whatever lies behind it. This dissertation is concerned with methods for analyzing multiple layer motion in microscopy, a domain where most objects are semitransparent. I present a novel approach to estimating the transmission of light through stationary, semitransparent objects by estimating the gradient of the constant transmission observed over all frames in a video. This enables removing the non-moving elements from the video, providing an enhanced view of the moving elements. I present a novel structured illumination technique that introduces a semitransparent pattern layer to microscopy, enabling microscope stage tracking even in the presence of stationary, sparse, or moving specimens. Magnitude comparisons at the frequencies present in the pattern layer provide estimates of pattern orientation and focal depth. Two pattern tracking techniques are examined, one based on phase correlation at pattern frequencies, and one based on spatial correlation using a model of pattern layer appearance based on microscopy image formation. Finally, I present a method for designing optimal structured illumination patterns tuned for constraints imposed by specific microscopy experiments. This approach is based on analysis of the microscope's optical transfer function at different focal depths
