A Novel Edge Detection Technique for Multi-Focus Images Using Image Fusion

Abstract

Since last three decades, edge detection techniques have been drawing the attention of researchers due to its applications in 3D reconstruction, motion recognition, morphing, restoration, watermarking, image compression, and so on. Again, detecting edges in multi-focused images are one of the most challenging tasks. This paper deals with a novel edge detection approach for multi-focused images by means of complex wavelet-based image fusion. An illumination-invariant hyperbolic tangent filter (HBT) is applied followed by an adaptive thresholding to get the real edges. The shift invariance and directionally selective diagonal filtering as well as the ease of implementation of dual-tree complex wavelet transform (DT-CWT) ensure robust sub-band fusion. It helps in avoiding the ringing artifacts that are more pronounced in discrete wavelet transform (DWT). The fusion using DT-CWT also solves the problem of low contrast and blocking effects. To fulfill the symmetry of sub-sampling structure and bi-orthogonal property, a Q-shift dual-tree CWT has been implemented. In the adaptive thresholding technique, the threshold value varies smartly over the image. This helps to combat with a potent illumination gradient, shadowing, and multi-focus blurring of an image.