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.
We describe the mathematical and conceptual foundations for a novel methodology for jointly optimizing the design and analysis of the optics, detector, and digital image processing for imaging systems. Our methodology is based on the end-to-end merit function of predicted average pixel sum-squared error to find the optical and image processing parameters that minimize this merit function. Our approach offers several advantages over the traditional principles of optical design, such as improved imaging performance, expanded operating capabilities, and improved as-built performance.