  Spline wavelets have shown favorable characteristics for localizing in both
time and frequency. In this paper, we propose a new biorthogonal cubic special
spline wavelet (BCSSW), based on the Cohen-Daubechies-Feauveau wavelet
construction method and the cubic special spline algorithm. BCSSW has better
properties in compact support, symmetry, and frequency domain characteristics.
However, current mainstream detection operators usually ignore the uncertain
representation of regional pixels and global structures. To solve these
problems, we propose a structural uncertainty-aware and multi-structure
operator fusion detection algorithm (EDBSW) based on a new BCSSW spline
wavelet. By constructing a spline wavelet that efficiently handles edge
effects, we utilize structural uncertainty-aware modulus maxima to detect
highly uncertain edge samples. The proposed wavelet detection operator utilizes
the multi-structure morphological operator and fusion reconstruction strategy
to effectively address anti-noise processing and edge information of different
frequencies. Numerous experiments have demonstrated its excellent performance
in reducing noise and capturing edge structure details.
