ReconfigISP: Reconfigurable Camera Image Processing Pipeline

TL;DR

ReconfigISP introduces a reconfigurable, differentiable neural architecture search-based approach to adapt camera image processing pipelines to diverse sensors, scenes, and tasks, improving flexibility and efficiency.

Contribution

It presents a novel differentiable, reconfigurable ISP architecture that can be automatically tailored to specific data and tasks using neural architecture search.

Findings

Effective across image restoration and object detection tasks

Requires only hundreds of parameters to tune per task

Validated on various sensors and lighting conditions

Abstract

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.