A Deep Learning Based No-Reference Image Quality Assessment Model for Single-Image Super-Resolution

Bahetiyaer Bare; Ke Li; Bo Yan; Bailan Feng; Chunfeng Yao

ICASSP 2018accepted0 citations

A Deep Learning Based No-Reference Image Quality Assessment Model for Single-Image Super-Resolution

Bahetiyaer Bare, Ke Li, Bo Yan, Bailan Feng, Chunfeng Yao

Abstract

Single-image super-resolution (SISR) is a very important and classic problem of the computer vision community. Although a lot of SISR methods have been proposed, few studies have been conducted to address the quality assessment of SISR methods. In this paper, we proposed a deep learning based no-reference image quality assessment (NR-IQA) model for SISR. We took small patches from images to form our training set and labeled them with different scores. With the aid of well-designed architecture and training strategy, our method achieved a performance leap than state-of-the-art methods. Experimental results proved the generalizability and the effectiveness of the proposed model.

BibTeX

@inproceedings{icassp2018_adeeplearningbas,
  title = {A Deep Learning Based No-Reference Image Quality Assessment Model for Single-Image Super-Resolution},
  author = {Bahetiyaer Bare and Ke Li and Bo Yan and Bailan Feng and Chunfeng Yao},
  booktitle = {ICASSP 2018},
  year = {2018}
}