# Exploring the Effect of Sparse Recovery on the Quality of Image Superresolution

arXiv: https://arxiv.org/abs/2308.02714 · HTML: https://mlsanity.com/paper/2308.02714/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 76 references that are in the map.
- First posted on arXiv 4 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Quality Assessment of Image Super-Resolution: Balancing Deterministic and Statistical Fidelity](https://mlsanity.com/paper/2207.08689/) (2022)
- [Using Task Descriptions in Lifelong Machine Learning for Improved Performance and Zero-Shot Transfer](https://mlsanity.com/paper/1710.03850/) (2017)
- [Coupled Dictionary Learning for Multi-contrast MRI Reconstruction](https://mlsanity.com/paper/1806.09930/) (2018)
- [Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art](https://mlsanity.com/paper/2111.03260/) (2021)
- [Image Super-Resolution Quality Assessment: Structural Fidelity Versus Statistical Naturalness](https://mlsanity.com/paper/2105.07139/) (2021)
- [Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition](https://mlsanity.com/paper/1709.05412/) (2017)
- [Zero-Shot Image Classification Using Coupled Dictionary Embedding](https://mlsanity.com/paper/1906.10509/) (2019)
- [Joint Dictionaries for Zero-Shot Learning](https://mlsanity.com/paper/1709.03688/) (2017)
- [Learning-Based Quality Assessment for Image Super-Resolution](https://mlsanity.com/paper/2012.08732/) (2020)
- [Testing fine-grained parallelism for the ADMM on a factor-graph](https://mlsanity.com/paper/1603.02526/) (2016)
- [Toward Real-Time Image Annotation Using Marginalized Coupled Dictionary Learning](https://mlsanity.com/paper/2304.06907/) (2023)

## Built on by (most cited first, 1 of 1)

- [Dy-mer: An Explainable DNA Sequence Representation Scheme using Dictionary Learning](https://mlsanity.com/paper/2407.12051/) (2024)

## Abstract (arXiv)

Dictionary learning can be used for image superresolution by learning a pair of coupled dictionaries of image patches from high-resolution and low-resolution image pairs such that the corresponding pairs share the same sparse vector when represented by the coupled dictionaries. These dictionaries then can be used to to reconstruct the corresponding high-resolution patches from low-resolution input images based on sparse recovery. The idea is to recover the shared sparse vector using the low-resolution dictionary and then multiply it by the high-resolution dictionary to recover the corresponding high-resolution image patch. In this work, we study the effect of the sparse recovery algorithm that we use on the quality of the reconstructed images. We offer empirical experiments to search for the best sparse recovery algorithm that can be used for this purpose.