# Multi-directional Sobel operator kernel on GPUs

arXiv: https://arxiv.org/abs/2305.00515 · HTML: https://mlsanity.com/paper/2305.00515/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Apr 2023.

## Built on by (most cited first, 2 of 2)

- [Video2Act: A Dual-System Video Diffusion Policy with Robotic Spatio-Motional Modeling](https://mlsanity.com/paper/2512.03044/) (2025)
- [Faster than Fast: Accelerating Oriented FAST Feature Detection on Low-end Embedded GPUs](https://mlsanity.com/paper/2506.07164/) (2025)

## Abstract (arXiv)

Sobel is one of the most popular edge detection operators used in image processing. To date, most users utilize the two-directional 3x3 Sobel operator as detectors because of its low computational cost and reasonable performance. Simultaneously, many studies have been conducted on using large multi-directional Sobel operators to satisfy their needs considering the high stability, but at an expense of speed. This paper proposes a fast graphics processing unit (GPU) kernel for the four-directional 5x5 Sobel operator. To improve kernel performance, we implement the kernel based on warp-level primitives, which can significantly reduce the number of memory accesses. In addition, we introduce the prefetching mechanism and operator transformation into the kernel to significantly reduce the computational complexity and data transmission latency. Compared with the OpenCV-GPU library, our kernel shows high performances of 6.7x speedup on a Jetson AGX Xavier GPU and 13x on a GTX 1650Ti GPU.