# Studying Very Low Resolution Recognition Using Deep Networks

arXiv: https://arxiv.org/abs/1601.04153 · HTML: https://mlsanity.com/paper/1601.04153/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2016.

## Built on by (most cited first, 30 of 36)

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- [Deep Rival Penalized Competitive Learning for Low-resolution Face Recognition](https://mlsanity.com/paper/2108.01286/) (2021)
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- [Unsupervised domain adaptation for clinician pose estimation and instance segmentation in the operating room](https://mlsanity.com/paper/2108.11801/) (2021)
- [Privacy-Preserving Person Detection Using Low-Resolution Infrared Cameras](https://mlsanity.com/paper/2209.11335/) (2022)
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- [Task-Driven Deep Image Enhancement Network for Autonomous Driving in Bad Weather](https://mlsanity.com/paper/2110.07206/) (2021)
- [Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection](https://mlsanity.com/paper/2208.03062/) (2022)
- [Efficient Low-Resolution Face Recognition via Bridge Distillation](https://mlsanity.com/paper/2409.11786/) (2024)
- [Look One and More: Distilling Hybrid Order Relational Knowledge for Cross-Resolution Image Recognition](https://mlsanity.com/paper/2409.05384/) (2024)
- [Performance Evaluation of Action Recognition Models on Low Quality Videos](https://mlsanity.com/paper/2204.09166/) (2022)
- [LoReTrack: Efficient and Accurate Low-Resolution Transformer Tracking](https://mlsanity.com/paper/2405.17660/) (2024)
- [Low-Resolution Object Recognition with Cross-Resolution Relational Contrastive Distillation](https://mlsanity.com/paper/2409.02555/) (2024)
- [GameTileNet: A Semantic Dataset for Low-Resolution Game Art in Procedural Content Generation](https://mlsanity.com/paper/2507.02941/) (2025)
- [DeepMoM: Robust Deep Learning With Median-of-Means](https://mlsanity.com/paper/2105.14035/) (2021)
- [Crafter: Facial Feature Crafting against Inversion-based Identity Theft on Deep Models](https://mlsanity.com/paper/2401.07205/) (2024)
- [Learning degraded image classification with restoration data fidelity](https://mlsanity.com/paper/2101.09606/) (2021)
- [Evaluating the Impact of Loss Function Variation in Deep Learning for Classification](https://mlsanity.com/paper/2210.16003/) (2022)
- [Degradation-based augmented training for robust individual animal re-identification](https://mlsanity.com/paper/2603.04163/) (2026)
- [Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition](https://mlsanity.com/paper/2101.10845/) (2021)
- [Class Equilibrium using Coulomb's Law](https://mlsanity.com/paper/2104.12287/) (2021)
- [Joint Optical Neuroimaging Denoising with Semantic Tasks](https://mlsanity.com/paper/2109.10499/) (2021)
- [RestoreDet: Degradation Equivariant Representation for Object Detection in Low Resolution Images](https://mlsanity.com/paper/2201.02314/) (2022)

## Abstract (arXiv)

Visual recognition research often assumes a sufficient resolution of the region of interest (ROI). That is usually violated in practice, inspiring us to explore the Very Low Resolution Recognition (VLRR) problem. Typically, the ROI in a VLRR problem can be smaller than 16 × 16 pixels, and is challenging to be recognized even by human experts. We attempt to solve the VLRR problem using deep learning methods. Taking advantage of techniques primarily in super resolution, domain adaptation and robust regression, we formulate a dedicated deep learning method and demonstrate how these techniques are incorporated step by step. Any extra complexity, when introduced, is fully justified by both analysis and simulation results. The resulting textitRobust Partially Coupled Networks achieves feature enhancement and recognition simultaneously. It allows for both the flexibility to combat the LR-HR domain mismatch, and the robustness to outliers. Finally, the effectiveness of the proposed models is evaluated on three different VLRR tasks, including face identification, digit recognition and font recognition, all of which obtain very impressive performances.