Make My Day - High-Fidelity Color Denoising With Near-Infrared

Hiroto Honda, Radu Timofte, Luc Van Gool; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015, pp. 82-90

We address the task of restoring RGB images taken under low illumination (e.g. night time), when an aligned near infrared (NIR or simply N) image taken under stronger NIR illumination is available. Such restoration holds the promise that algorithms designed to work under daylight conditions could be used around the clock. Increasingly, RGBN cameras are becoming available, as car cameras tend to include a Near-Infrared (N) band, next to R, G, and B bands, and NIR artificial lighting is applied. Under low lighting conditions, the NIR band is less noisy than the others and this is all the more the case if stronger illumination is only available in the NIR band. We address the task of restoring the R, G, and B bands on the basis of the NIR band in such cases. Even if the NIR band is less strongly correlated with the R, G, and B bands than these bands are mutually, there is sufficient such correlation to pick up important textural and gradient information in the NIR band and inject it into the others. The algorithm that we propose - coined `Make My Day' or MMD for short - is akin to the previously published BM3D denoising algorithm. MMD denoises the three (visible - NIR) differential images to then add back the original NIR image. It not only effectively reduces the noise but also includes the texture and edge information in the high spatial frequency range. MMD outperforms other state-of-art denoising methods in terms of PSNR, texture quality, and color fidelity. We publish our codes and images.