BALF: Simple and Efficient Blur Aware Local Feature Detector

TL;DR

This paper introduces BALF, a lightweight, real-time keypoint detector designed to accurately identify salient features in blurred images, improving detection repeatability under low-light conditions without sacrificing performance on sharp images.

Contribution

The paper presents a novel MLP-based architecture for blur-aware local feature detection, enhancing repeatability in blurred images while maintaining efficiency and real-time capability.

Findings

Improves detection repeatability in blurred images.

Maintains comparable performance on sharp images.

Runs in real-time for practical applications.

Abstract

Local feature detection is a key ingredient of many image processing and computer vision applications, such as visual odometry and localization. Most existing algorithms focus on feature detection from a sharp image. They would thus have degraded performance once the image is blurred, which could happen easily under low-lighting conditions. To address this issue, we propose a simple yet both efficient and effective keypoint detection method that is able to accurately localize the salient keypoints in a blurred image. Our method takes advantages of a novel multi-layer perceptron (MLP) based architecture that significantly improve the detection repeatability for a blurred image. The network is also light-weight and able to run in real-time, which enables its deployment for time-constrained applications. Extensive experimental results demonstrate that our detector is able to improve the detection repeatability with blurred images, while keeping comparable performance as existing state-of-the-art detectors for sharp images.