# Superkernel Neural Architecture Search for Image Denoising

Source: [Superkernel Neural Architecture Search for Image Denoising](<https://www.sota2.com/research/paper/68fa675b1e209131cea15cc6>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:42.488Z

Authors: Marcin Mo\\.zejko, Tomasz Latkowski, {\\L}ukasz Treszczotko, Micha{\\l} Szafraniuk, Krzysztof Trojanowski

Published: 2020-04-19T14:52:22.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2004.08870>)

[PDF](<https://arxiv.org/pdf/2004.08870.pdf>)