Edge detection refers to the process of identifying as well as locating sharp discontinuities in an image. While preserving the essential structural properties in an image, edge detection process significantly reduces the amount of data and filters out useless information. Due to its superior performance, the Canny edge detector is one of the most widely used edge detection algorithms. Compared with other edge detection algorithms, it is computationally more intensive and it also has a higher latency as it is based on frame-level statistics.
The author in this paper proposes at the block level, compared to the original frame-level Canny algorithm, without any loss in edge detection efficiency. The application of the original Canny algorithm directly at the block level leads to the loss of excessive edges in smooth regions, as well as the loss of substantial edges in extremely detailed regions, since the original Canny calculates the high and low thresholds on the basis of the statistics for the frame level. A modified Canny edge detection algorithm is provided, which calculates the edge detection thresholds adaptively based on the type of block. In addition, a substantially reduced area and increased frequency will be given by the resulting block-based algorithm. Additionally, the efficiency of the proposed algorithm’s edge detection will be higher than the current algorithm. As more useful edge knowledge can be retained, the proposed algorithm can produce better results than the current algorithm.