# Decouple Learning for Parameterized Image Operators

Source: [Decouple Learning for Parameterized Image Operators](<https://www.sota2.com/research/paper/68fa64be1e209131cea063a7>)

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

Data updated: 2025-10-27T05:55:49.570Z

Authors: Qingnan Fan, Dongdong Chen, Lu Yuan, Gang Hua, Nenghai Yu, Baoquan Chen

Published: 2018-07-21T17:58:54.000Z

## Abstract

Many different deep networks have been used to approximate, accelerate or improve traditional image operators, such as image smoothing, super-resolution and denoising. Among these traditional operators, many contain parameters which need to be tweaked to obtain the satisfactory results, which we refer to as "parameterized image operators". However, most existing deep networks trained for these operators are only designed for one specific parameter configuration, which does not meet the needs of real scenarios that usually require flexible parameters settings. To overcome this limitation, we propose a new decouple learning algorithm to learn from the operator parameters to dynamically adjust the weights of a deep network for image operators, denoted as the base network. The learned algorithm is formed as another network, namely the weight learning network, which can be end-to-end jointly trained with the base network. Experiments demonstrate that the proposed framework can be successfully applied to many traditional parameterized image operators. We provide more analysis to better understand the proposed framework, which may inspire more promising research in this direction. Our codes and models have been released in https://github.com/fqnchina/DecoupleLearning

## 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.

- [Raw-to-raw translation on Nikon Z6 (test)](<https://www.sota2.com/research/sota/raw-to-raw-translation-on-nikon-z6-test.md>): Raw-to-raw translation; Nikon Z6 (test); PSNR: 34.34.

- [Raw-to-raw translation on Canon 70D (test)](<https://www.sota2.com/research/sota/raw-to-raw-translation-on-canon-70d-test.md>): Raw-to-raw translation; Canon 70D (test); PSNR: 24.86.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1807.08186>)

[PDF](<https://arxiv.org/pdf/1807.08186.pdf>)