StereoISP: Rethinking Image Signal Processing for Dual Camera Systems

TL;DR

StereoISP introduces a novel image signal processing framework that leverages raw measurements from stereo cameras to enhance RGB image reconstruction, showing promising preliminary improvements in image quality.

Contribution

It proposes a new ISP framework that integrates stereo raw data and disparity estimation to improve image reconstruction quality.

Findings

At least 2dB PSNR improvement on KITTI 2015 and drivingStereo datasets.

Utilizes disparity maps to enhance demosaicking and denoising.

Preliminary results indicate potential for better multi-camera image processing.

Abstract

Conventional image signal processing (ISP) frameworks are designed to reconstruct an RGB image from a single raw measurement. As multi-camera systems become increasingly popular these days, it is worth exploring improvements in ISP frameworks by incorporating raw measurements from multiple cameras. This manuscript is an intermediate progress report of a new ISP framework that is under development, StereoISP. It employs raw measurements from a stereo camera pair to generate a demosaicked, denoised RGB image by utilizing disparity estimated between the two views. We investigate StereoISP by testing the performance on raw image pairs synthesized from stereo datasets. Our preliminary results show an improvement in the PSNR of the reconstructed RGB image by at least 2dB on KITTI 2015 and drivingStereo datasets using ground truth sparse disparity maps.