  Many creative ideas are being proposed for image sensor designs, and these
may be useful in applications ranging from consumer photography to computer
vision. To understand and evaluate each new design, we must create a
corresponding image processing pipeline that transforms the sensor data into a
form that is appropriate for the application. The need to design and optimize
these pipelines is time-consuming and costly. We explain a method that combines
machine learning and image systems simulation that automates the pipeline
design. The approach is based on a new way of thinking of the image processing
pipeline as a large collection of local linear filters. We illustrate how the
method has been used to design pipelines for novel sensor architectures in
consumer photography applications.
