GenISP: Neural ISP for Low-Light Machine Cognition

TL;DR

GenISP is a neural image signal processing pipeline designed for low-light conditions that improves object detection performance and generalizes across different camera sensors by optimizing for machine cognition rather than perceptual quality.

Contribution

The paper introduces GenISP, a minimal neural ISP pipeline with device-independent color space transformation, trained with a pre-trained detector, enhancing generalization to unseen sensors and detectors.

Findings

GenISP outperforms traditional methods in low-light object detection.

GenISP generalizes well to unseen camera sensors.

A new low-light dataset with annotations is provided for benchmarking.

Abstract

Object detection in low-light conditions remains a challenging but important problem with many practical implications. Some recent works show that, in low-light conditions, object detectors using raw image data are more robust than detectors using image data processed by a traditional ISP pipeline. To improve detection performance in low-light conditions, one can fine-tune the detector to use raw image data or use a dedicated low-light neural pipeline trained with paired low- and normal-light data to restore and enhance the image. However, different camera sensors have different spectral sensitivity and learning-based models using raw images process data in the sensor-specific color space. Thus, once trained, they do not guarantee generalization to other camera sensors. We propose to improve generalization to unseen camera sensors by implementing a minimal neural ISP pipeline for machine cognition, named GenISP, that explicitly incorporates Color Space Transformation to a device-independent color space. We also propose a two-stage color processing implemented by two image-to-parameter modules that take down-sized image as input and regress global color correction parameters. Moreover, we propose to train our proposed GenISP under the guidance of a pre-trained object detector and avoid making assumptions about perceptual quality of the image, but rather optimize the image representation for machine cognition. At the inference stage, GenISP can be paired with any object detector. We perform extensive experiments to compare our method to other low-light image restoration and enhancement methods in an extrinsic task-based evaluation and validate that GenISP can generalize to unseen sensors and object detectors. Finally, we contribute a low-light dataset of 7K raw images annotated with 46K bounding boxes for task-based benchmarking of future low-light image restoration and object detection.