HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization

TL;DR

HDRUNet is a novel deep learning model that reconstructs high dynamic range images from single low-quality images, effectively reducing noise and quantization errors, outperforming existing methods in quality and winning a challenge.

Contribution

The paper introduces HDRUNet, a new end-to-end neural network architecture for single image HDR reconstruction with denoising and dequantization, featuring a spatially dynamic encoder-decoder design and a novel loss function.

Findings

Achieves state-of-the-art quantitative and visual results.

Outperforms previous methods in HDR reconstruction quality.

Secures second place in NITRE2021 HDR challenge.

Abstract

Most consumer-grade digital cameras can only capture a limited range of luminance in real-world scenes due to sensor constraints. Besides, noise and quantization errors are often introduced in the imaging process. In order to obtain high dynamic range (HDR) images with excellent visual quality, the most common solution is to combine multiple images with different exposures. However, it is not always feasible to obtain multiple images of the same scene and most HDR reconstruction methods ignore the noise and quantization loss. In this work, we propose a novel learning-based approach using a spatially dynamic encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single image HDR reconstruction with denoising and dequantization. The network consists of a UNet-style base network to make full use of the hierarchical multi-scale information, a condition network to perform pattern-specific modulation and a weighting network for selectively retaining information. Moreover, we propose a Tanh_L1 loss function to balance the impact of over-exposed values and well-exposed values on the network learning. Our method achieves the state-of-the-art performance in quantitative comparisons and visual quality. The proposed HDRUNet model won the second place in the single frame track of NITRE2021 High Dynamic Range Challenge.