Delineating patterns that are alike or in other words, detecting edges that separates them is the most critical step in image processing. There are various methods available e.g Sobel, Prewitt, Canny based edge detection etc. But most of them are either time consuming or allergic to noise levels and therefore tradeoff comes into the context where we need to choose between the methods that are less computationally expensive with those that provide clear edges. Techniques that allows finding edges as clear as possible without compromising with the computational load is always preferable. Here, I report a novel edge detection technique: abs-Laplacian which has reduced complexity of 7N that requires nearly half the amount of computation involved in Sobel and Prewit which has 15N and 13N complexity respectively. Further, quality of the edges does not show any significant difference with the either of them suggesting that the technique is better both in terms of speed and edge quality while the technique doesn’t show up any aberrant fluctuations in the intensity plots and that the derivatives are bit lesser and wider in comparison to the peaks observable upon Sobel and Prewitt treatments, it further suggests that the method may hold some key potential in negating the noise levels to an extent, but subject to further analysis.