# Generative Collaborative Networks for Single Image Super-Resolution

Source: [Generative Collaborative Networks for Single Image Super-Resolution](<https://www.sota2.com/research/paper/68fa65851e209131cea0aca6>)

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

Data updated: 2025-10-27T05:57:48.173Z

Authors: Mohamed El Amine Seddik, Mohamed Tamaazousti, John Lin

Published: 2019-02-27T11:34:10.000Z

## Abstract

A common issue of deep neural networks-based methods for the problem of Single Image Super-Resolution (SISR), is the recovery of finer texture details when super-resolving at large upscaling factors. This issue is particularly related to the choice of the objective loss function. In particular, recent works proposed the use of a VGG loss which consists in minimizing the error between the generated high resolution images and ground-truth in the feature space of a Convolutional Neural Network (VGG19), pre-trained on the very "large" ImageNet dataset. When considering the problem of super-resolving images with a distribution "far" from the ImageNet images distribution (\\textit{e.g.,} satellite images), their proposed \\textit{fixed} VGG loss is no longer relevant. In this paper, we present a general framework named \\textit{Generative Collaborative Networks} (GCN), where the idea consists in optimizing the \\textit{generator} (the mapping of interest) in the feature space of a \\textit{features extractor} network. The two networks (generator and extractor) are \\textit{collaborative} in the sense that the latter "helps" the former, by constructing discriminative and relevant features (not necessarily \\textit{fixed} and possibly learned \\textit{mutually} with the generator). We evaluate the GCN framework in the context of SISR, and we show that it results in a method that is adapted to super-resolution domains that are "far" from the ImageNet domain.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1902.10467>)

[PDF](<https://arxiv.org/pdf/1902.10467.pdf>)