BALF: Simple and Efficient Blur Aware Local Feature Detector
This addresses a practical issue for computer vision applications like visual odometry under low-lighting conditions, but it is incremental as it builds on existing feature detection methods.
The paper tackled the problem of degraded local feature detection in blurred images, proposing a blur-aware detector that improves repeatability for blurred images while maintaining comparable performance on sharp images.
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.