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A Review on Self Learning Based Methods for Real World Single Image Super Resolution

EasyChair Preprint no. 6721

15 pagesDate: September 29, 2021

Abstract

Image super resolution (ISR) is one of the popular techniques of image processing to boost the resolution of images. Reconstructing high-resolution (HR) image from low resolution (LR) degraded images results in Single image super resolution (RSISR) reconstruction. In the domain of image processing, it is the lively research topic. This paper covers datasets which are available and assessment metrics for RSISR and method of RSISR based on Self-Learning RSISR [1]. In terms of both reconstruction quality and computational efficiency comparisons are done among representative RSISR methods on datasets. We will discuss challenges on RSISR.

Keyphrases: computer vision, datasets, deep learning, Real-world image, Self-learning-based methods, super-resolution

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@Booklet{EasyChair:6721,
  author = {Yogesh Gaikwad},
  title = {A Review on Self Learning Based Methods for Real World Single Image Super Resolution},
  howpublished = {EasyChair Preprint no. 6721},

  year = {EasyChair, 2021}}
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