# Assessing the Impact of Image Super Resolution on White Blood Cell Classification Accuracy

arXiv: https://arxiv.org/abs/2508.03759 · HTML: https://mlsanity.com/paper/2508.03759/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 4 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects](https://mlsanity.com/paper/2004.02806/) (2020)
- [Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future](https://mlsanity.com/paper/2001.07092/) (2020)
- [Explorable Super Resolution](https://mlsanity.com/paper/1912.01839/) (2019)
- [Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-Resolution](https://mlsanity.com/paper/2005.00953/) (2020)
- [Convolutional Neural Networks based Focal Loss for Class Imbalance Problem: A Case Study of Canine Red Blood Cells Morphology Classification](https://mlsanity.com/paper/2001.03329/) (2020)
- [A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators](https://mlsanity.com/paper/2112.10046/) (2021)
- [An Efficient Convolutional Neural Network for Coronary Heart Disease Prediction](https://mlsanity.com/paper/1909.00489/) (2019)
- [Deep CNNs for Peripheral Blood Cell Classification](https://mlsanity.com/paper/2110.09508/) (2021)
- [Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN](https://mlsanity.com/paper/2201.06383/) (2022)
- [A survey on automated detection and classification of acute leukemia and WBCs in microscopic blood cells](https://mlsanity.com/paper/2303.03916/) (2023)
- [Tarsier: Evolving Noise Injection in Super-Resolution GANs](https://mlsanity.com/paper/2009.12177/) (2020)

## Abstract (arXiv)

Accurately classifying white blood cells from microscopic images is essential to identify several illnesses and conditions in medical diagnostics. Many deep learning technologies are being employed to quickly and automatically classify images. However, most of the time, the resolution of these microscopic pictures is quite low, which might make it difficult to classify them correctly. Some picture improvement techniques, such as image super-resolution, are being utilized to improve the resolution of the photos to get around this issue. The suggested study uses large image dimension upscaling to investigate how picture-enhancing approaches affect classification performance. The study specifically looks at how deep learning models may be able to understand more complex visual information by capturing subtler morphological changes when image resolution is increased using cutting-edge techniques. The model may learn from standard and augmented data since the improved images are incorporated into the training process. This dual method seeks to comprehend the impact of image resolution on model performance and enhance classification accuracy. A well-known model for picture categorization is used to conduct extensive testing and thoroughly evaluate the effectiveness of this approach. This research intends to create more efficient image identification algorithms customized to a particular dataset of white blood cells by understanding the trade-offs between ordinary and enhanced images.