Joint Denoising and Fusion with Short- and Long-exposure Raw Pairs

Denoising and high dynamic range (HDR) imaging are significant yet challenging problems due to the small aperture and sensor size of generic image sensors. Current methods predominantly generate HDR images from a set of bracketed exposure sRGB images. However, they overlook the computational and memory inefficiencies of the Image Signal Processor (ISP) when processing a set of sRGB images with different exposures. Furthermore, the absence of large-scale raw-based HDR datasets limits the research on HDR imaging. Unlike existing methods, the core idea of this work is to utilize the difference between short- and long-exposure images of signal-to-noise ratios to generate HDR images and denoising. To this end, we propose a model tailor-made for double-exposure HDR sensors, leveraging the unique features of the raw data to facilitate raw-to-HDR mapping and raw denoising. Our key insights are threefold: (1) a new computational raw LDR-HDR pair formation pipeline is designed to construct a real-world raw HDR dataset called RealRaw-HDR; (2) a lightweight-efficient HDR model, RepUNet, is developed using the structural reparameterization technique; (3) a plug-and-play alignment-free and motion-aware short-exposure-first selection loss and a colorfulness loss are proposed to mitigate ghost artifacts and color cast. Our empirical evaluation validates the effectiveness of the proposed LDR-HDR formation pipeline, as well as experiments show that our method achieves comparable performance to the state-of-the-art methods with less computational cost.