# Superkernel Neural Architecture Search for Image Denoising

arXiv: https://arxiv.org/abs/2004.08870 · HTML: https://mlsanity.com/paper/2004.08870/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 4 of 4)

- [Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization](https://mlsanity.com/paper/2403.17012/) (2024)
- [Searching Efficient Model-guided Deep Network for Image Denoising](https://mlsanity.com/paper/2104.02525/) (2021)
- [A Sub-band Approach to Deep Denoising Wavelet Networks and a Frequency-adaptive Loss for Perceptual Quality](https://mlsanity.com/paper/2102.07973/) (2021)
- [Pushing Joint Image Denoising and Classification to the Edge](https://mlsanity.com/paper/2409.08943/) (2024)

## Abstract (arXiv)

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.