Methods › Computer Vision › Image Super-Resolution Models › ClassSR

ClassSR

1 paper tagged archive 2025-07-28

Introduced by Xiangtao Kong et al. in ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

ClassSR is a framework to accelerate super-resolution (SR) networks on large images (2K-8K). ClassSR combines classification and SR in a unified framework. In particular, it first uses a Class-Module to classify the sub-images into different classes according to restoration difficulties, then applies an SR-Module to perform SR for different classes. The Class-Module is a conventional classification network, while the SR-Module is a network container that consists of the to-be-accelerated SR network and its simplified versions.

PaperSource

Papers archive 2025-07-28

1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

5 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
2k1
8k1
Classification1
General Classification1
Super-Resolution1

Usage over time archive 2025-07-28

Papers per year tagged with ClassSR: 2021 to 2021, peak 1 1 0 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Image Super-Resolution Models

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