Learning efficient image processing pipelines

Abstract

The high resolution of modern cameras puts significant performance pressure on image processing pipelines. Tuning the parameters of these pipelines for speed is subject to stringent image quality constraints and requires significant efforts from skilled programmers. Because quality is driven by perceptual factors with which most quantitative image metrics correlate poorly, developing new pipelines involves long iteration cycles, alternating between software implementation and visual evaluation by human experts. These concerns are compounded on modern computing platforms, which are increasingly mobile and heterogeneous. In this dissertation, we apply machine learning towards the design of high-performance, high-fidelity algorithms whose parameters can be optimized automatically on large-scale datasets via gradient based methods. We present applications to low-level image restoration and high performance image filtering on mobile devices.