  An image processing unit (IPU), or image signal processor (ISP) for high
dynamic range (HDR) imaging usually consists of demosaicing, white balancing,
lens shading correction, color correction, denoising, and tone-mapping. Besides
noise from the imaging sensors, almost every step in the ISP introduces or
amplifies noise in different ways, and denoising operators are designed to
reduce the noise from these sources. Designed for dynamic range compressing,
tone-mapping operators in an ISP can significantly amplify the noise level,
especially for images captured in low-light conditions, making denoising very
difficult. Therefore, we propose a joint multi-scale denoising and tone-mapping
framework that is designed with both operations in mind for HDR images. Our
joint network is trained in an end-to-end format that optimizes both operators
together, to prevent the tone-mapping operator from overwhelming the denoising
operator. Our model outperforms existing HDR denoising and tone-mapping
operators both quantitatively and qualitatively on most of our benchmarking
datasets.
