Seismic data is traditionally acquired based on spatial sampling requirements, noise properties, and budgetary constraints. However, designing a survey without taking into account the complexity of the subsurface may result in an image without the expected quality. Also, the subsequent preprocessing and processing steps, may exploit or misuse the acquired data. The design should therefore incorporate the complexity of the subsurface and the (pre)processing steps that will be followed. We propose an analysis method that evaluates if the proposed combination of survey design, preprocessing and processing for a specific subsurface model fulfills a pre-defined quality criterion. With our method we estimate a set of point-spread functions that correspond to the chosen combination and we analyse their resolution and illumination-detection properties in the image and wavenumber domains, respectively. The estimated point-spread functions include the scattering and propagation effects generated by the subsurface, including internal multiples. We show that in some cases, the use of internal multiples in imaging can improve resolution and illumination-detection compared to the use of primaries only.