StereoISP: Rethinking Image Signal Processing for Dual Camera Systems
This work addresses image quality enhancement for multi-camera systems, such as in automotive or mobile applications, but it is incremental as it builds on existing stereo and ISP concepts.
The authors tackled the problem of improving image signal processing (ISP) for dual camera systems by proposing StereoISP, a framework that uses raw measurements from a stereo camera pair to generate demosaicked, denoised RGB images. Preliminary results show an improvement in PSNR by at least 2dB on KITTI 2015 and drivingStereo datasets using ground truth sparse disparity maps.
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.