Hardware-Aware Low-Light Image Enhancement via One-Shot Neural Architecture Search with Shrinkage Sampling

Low-light image enhancement has traditionally been tackled by training a heuristically designed neural network architecture. Despite the success of these approaches, the heuristic design pattern inherently not only hinders further optimization of network architectures, but also limits the factors that the designer can take into consideration. As a result, these methods are difficult to achieve a balance between enhancing performance and hardware related performance. In this paper, we equip a basic enhancing algorithm with a neural architecture search technique. This technique helps to automatically search an optimal hardware-aware architecture while also increases neglectable computation burden. In this work, we propose a shrinkage sampling strategy to drastically decrease the computation cost of neural architecture search while improving the quality of search. Extensive experiments on various benchmarks demonstrate that our algorithm achieves state-of-the-art performance with higher speed.