# Image edge enhancement for effective image classification

Source: [Image edge enhancement for effective image classification](<https://www.sota2.com/research/paper/68fa71df1e209131cea5379a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:29:09.138Z

Authors: Tianhao Bu, Michalis Lazarou, Tania Stathaki

Published: 2024-01-13T10:01:34.000Z

## Abstract

Image classification has been a popular task due to its feasibility in real-world applications. Training neural networks by feeding them RGB images has demonstrated success over it. Nevertheless, improving the classification accuracy and computational efficiency of this process continues to present challenges that researchers are actively addressing. A widely popular embraced method to improve the classification performance of neural networks is to incorporate data augmentations during the training process. Data augmentations are simple transformations that create slightly modified versions of the training data and can be very effective in training neural networks to mitigate overfitting and improve their accuracy performance. In this study, we draw inspiration from high-boost image filtering and propose an edge enhancement-based method as means to enhance both accuracy and training speed of neural networks. Specifically, our approach involves extracting high frequency features, such as edges, from images within the available dataset and fusing them with the original images, to generate new, enriched images. Our comprehensive experiments, conducted on two distinct datasets CIFAR10 and CALTECH101, and three different network architectures ResNet-18, LeNet-5 and CNN-9 demonstrates the effectiveness of our proposed method.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2401.07028>)

[PDF](<https://arxiv.org/pdf/2401.07028.pdf>)