Defocus Blur-Invariant Scale-Space Feature Extractions

• Published in: IEEE transactions on image processing Vol. 25; no. 7; pp. 3141 - 3156
• Main Authors: Saad, Elhusain, Hirakawa, Keigo
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cameras; Computer vision; Defocus Blur; Detectors; Feature extraction; Histograms; Image analysis; Image detection; Image quality; Invariants; Kernel; Kernels; Lighting; Robustness; SIFT; SURF; SURF; SIFT; defocus blur
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2016.2555702

We propose modifications to scale-space feature extraction techniques scale-invariant feature transform (SIFT) and speeded up robust features (SURFs) that make the feature detection and description invariant to defocus blur. Specifically, the scale-space blob detection relies on the second derivative responses of images. Our analysis of circular defocus blur (which sufficiently approximates a real camera blur kernel) and its effect on scale-space blob detection suggests that fourth derivative-and not the usual second derivative-is optimal for detecting the blurred blobs, while multi-scale descriptors of blurred blobs are effective at establishing correspondences between the blurred images. The proposed defocus blur-invariant (DBI) scale-space feature extraction techniques-which we refer to as DBI-SIFT and DBI-SURF-do not require image deblurring nor blur kernel estimation, meaning that their accuracy does not depend on the quality of image deblurring. We offer empirical evidence of blur invariance by establishing interest point correspondences between sharp or blurred reference images and blurred target images.