Preserving edges in a noisy environment is a challenging task as even some of the latest endto-end deep learning (DL) algorithms continue to struggle in achieving high pixel-level accuracy. As the Canny Edge Detector (CED) continues to be one of the most popular edge detection operators, this paper presents an enhanced CED using Stochastic Resonance (SR) guided threshold maneuvering and window mapping, which takes the same input parameter set as that of the conventional Canny but produces the edge map with better-connected edges and reduced noise. The SR-based analysis informs the steps that should be followed to enhance the performance of the classical CED. We also propose a new measure for efficient edge detection; a unique, efficient way of edge content extraction and its combination for various channels; and a framework to handle repercussions of the randomness of the noise. Since the proposed solution comes in the form of a modular patch-based framework, it can be easily incorporated into other algorithm developments. Qualitative and quantitative results are presented along with the BSDS500 & BIPED benchmarking to showcase the proposed algorithm’s effectiveness. On BIPED benchmarking, our algorithm gives the human-level performance (F1 score .79), which is appreciable considering that it is a non-DL–based algorithm.