  Image Signal Processor (ISP) is a crucial component in digital cameras that
transforms sensor signals into images for us to perceive and understand.
Existing ISP designs always adopt a fixed architecture, e.g., several
sequential modules connected in a rigid order. Such a fixed ISP architecture
may be suboptimal for real-world applications, where camera sensors, scenes and
tasks are diverse. In this study, we propose a novel Reconfigurable ISP
(ReconfigISP) whose architecture and parameters can be automatically tailored
to specific data and tasks. In particular, we implement several ISP modules,
and enable backpropagation for each module by training a differentiable proxy,
hence allowing us to leverage the popular differentiable neural architecture
search and effectively search for the optimal ISP architecture. A proxy tuning
mechanism is adopted to maintain the accuracy of proxy networks in all cases.
Extensive experiments conducted on image restoration and object detection, with
different sensors, light conditions and efficiency constraints, validate the
effectiveness of ReconfigISP. Only hundreds of parameters need tuning for every
task.
