The common goal of both tasks is to preserve or even enhance the details and improve the visibility of scenes when generating the HDR image. In this paper, we relate the operation of single-frame-based high dynamic range (HDR) image reconstruction to the following two tasks: 1) highlight suppression in over-exposed areas and 2) noise elimination in under-exposed areas. MQN models provide up to 10 times improvement on latency and 25 times improvement on memory consumption. In the comparative analyses, ITM models trained using MQN perform on par with the state-of-the-art methods on benchmark datasets. In the ablation studies, we explore the effect of using different attention mechanisms, quantization schemes, and loss functions on the performance of MQN in ITM tasks. To this end, we propose combining efficient operations of deep neural networks with a novel mixed quantization scheme to construct a well-performing but computationally efficient mixed quantization network (MQN) which can perform single image ITM on mobile platforms.

Current methods focus exclusively on training high-performing but computationally inefficient ITM models, which in turn hinder deployment of the ITM models in resource-constrained environments with limited computing power such as edge and mobile device applications. Recovering a high dynamic range (HDR) image from a single low dynamic range (LDR) image, namely inverse tone mapping (ITM), is challenging due to the lack of information in over- and under-exposed regions.

Hdrsoft photomatix pro 4.1.4