  Recent advancements in Neural Architecture Search(NAS) resulted in finding
new state-of-the-art Artificial Neural Network (ANN) solutions for tasks like
image classification, object detection, or semantic segmentation without
substantial human supervision. In this paper, we focus on exploring NAS for a
dense prediction task that is image denoising. Due to a costly training
procedure, most NAS solutions for image enhancement rely on reinforcement
learning or evolutionary algorithm exploration, which usually take weeks (or
even months) to train. Therefore, we introduce a new efficient implementation
of various superkernel techniques that enable fast (6-8 RTX2080 GPU hours)
single-shot training of models for dense predictions. We demonstrate the
effectiveness of our method on the SIDD+ benchmark for image denoising.
