Sobel operator
The Sobel operator, also known as the Sobel–Feldman operator, is a discrete differentiation gradient-based filter used in digital image processing for detecting edges by approximating the first-order derivatives of the image intensity function.[1] It employs two separate 3×3 convolution kernels—one for the horizontal gradient (Gx) and one for the vertical gradient (Gy)—to compute the rate of change in intensity along the x- and y-directions at each pixel, with the overall edge strength derived from the magnitude √(Gx2 + Gy2) and direction from the arctangent of Gy/Gx.[2] The horizontal kernel emphasizes vertical edges, while the vertical kernel highlights horizontal edges, and the operator's design incorporates central weighting to provide a measure of local smoothing, reducing noise sensitivity compared to simpler differencing methods.[3]
Developed by Irwin Sobel and Gary M. Feldman at the Stanford Artificial Intelligence Laboratory (SAIL), the operator was first presented in a 1968 internal talk titled "An Isotropic 3×3 Image Gradient Operator," marking an early contribution to computational edge detection techniques in computer vision. Although not formally published as a peer-reviewed paper at the time, it gained prominence through subsequent references in works like Pingle's 1969 edge-following algorithm and Duda and Hart's 1973 pattern classification book, establishing it as a foundational tool for gradient approximation. The kernels, which separate the smoothing (via averaging) and differentiation steps, are defined as follows:
Horizontal kernel (Gx):
[-1 0 1]
[-2 0 2]
[-1 0 1]
[-1 0 1]
[-2 0 2]
[-1 0 1]
Vertical kernel (Gy):
[-1 -2 -1]
[ 0 0 0]
[ 1 2 1]
[-1 -2 -1]
[ 0 0 0]
[ 1 2 1]
These are applied via convolution, often normalized by dividing by 8 (though sometimes omitted for simplicity in discrete implementations), to yield the partial derivatives.[4][2]
The Sobel operator's simplicity, low computational cost, and ability to detect edges in multiple orientations make it widely implemented in libraries like OpenCV and MATLAB, serving as a baseline for more advanced detectors such as Canny.[1] It excels in uniform lighting conditions but can produce thicker edges and be sensitive to noise without preprocessing like Gaussian smoothing, leading to its frequent combination with other filters in practical applications such as object recognition and medical imaging.[2] Despite the advent of sophisticated methods, its isotropic approximation of the gradient remains influential in both educational contexts and real-time systems due to its balance of accuracy and efficiency.[5]
Overview
Definition and Purpose
The Sobel operator is a discrete differentiation operator employed in image processing and computer vision to approximate the gradient of an image's intensity function at each pixel, thereby identifying edges as regions of rapid intensity variation. First described by Irwin Sobel and Gary M. Feldman in 1968, it utilizes two separate 3×3 kernels: one to compute the horizontal gradient component G_x and the other to compute the vertical gradient component G_y.[6]
The primary purpose of the Sobel operator is to detect edges in digital images by emphasizing abrupt changes in pixel intensity, which correspond to boundaries between distinct objects or regions; this makes it a foundational tool for feature extraction in computer vision pipelines, such as object recognition and segmentation tasks. In practice, it generates an edge strength map by combining the magnitudes of the horizontal and vertical gradients, typically through a vector magnitude computation that highlights the overall rate of intensity change regardless of direction.[7]
A key strength of the Sobel operator lies in its computational efficiency, achieved via the compact 3×3 kernel size and separable convolution that allows for optimized integer-based operations, enabling fast processing even on resource-constrained systems. Additionally, it exhibits greater noise tolerance than simpler first-derivative methods or higher-order operators, as the kernel's weighted design incorporates smoothing over neighboring pixels to suppress high-frequency noise while preserving edge information.[3][8]
Historical Development
The Sobel operator was developed in 1968 by Irwin Sobel, then a PhD candidate, and Gary M. Feldman at the Stanford Artificial Intelligence Laboratory (SAIL), as a component of broader scene analysis systems aimed at machine perception.[9] This work emerged amid the 1960s surge in digital image processing research to enable automated visual interpretation of environments, including early explorations of edge detection and motion analysis techniques such as optical flow. Sobel and Feldman's contribution built on these foundations, introducing a simple yet effective method for gradient approximation tailored to discrete image data.[9]
The initial description of the operator appeared in a 1968 presentation titled A 3x3 Isotropic Gradient Operator for Image Processing delivered at SAIL, which highlighted its role in enhancing edge responses while smoothing noise.[9] Though the talk itself remained unpublished, the concept was documented in subsequent SAIL technical reports. By the 1970s, the operator gained prominence through its adoption in foundational computer vision algorithms for pattern recognition and scene understanding, marking a shift toward practical implementations in early digital vision systems.
In the ensuing decades, the Sobel operator's simplicity and efficiency propelled its integration into prominent software frameworks, such as MATLAB's Image Processing Toolbox and OpenCV's core library, where it serves as a standard tool for gradient-based processing.[1] Even with the rise of sophisticated alternatives like the Canny detector in the 1980s, the operator retains enduring relevance in resource-constrained real-time applications, such as embedded vision systems and hardware-accelerated edge detection on FPGAs, due to its low computational overhead and robustness to moderate noise.
Core Kernels
The Sobel operator employs two fundamental 3×3 convolution kernels to approximate the partial derivatives of the image intensity in the horizontal and vertical directions, respectively. These kernels were introduced in the original formulation as a discrete approximation to the image gradient for edge detection.[10]
The horizontal kernel G_x, designed to detect vertical edges by emphasizing intensity changes along the x-axis, takes the form:
\begin{bmatrix}
-1 & 0 & 1 \\
-2 & 0 & 2 \\
-1 & 0 & 1
\end{bmatrix}
[7]
This structure computes the difference between pixels on the right and left sides of the center, with doubled weights in the middle row to prioritize the central horizontal differences.[10]
The vertical kernel G_y, used for detecting horizontal edges by capturing changes along the y-axis, is defined as:
\begin{bmatrix}
-1 & -2 & -1 \\
0 & 0 & 0 \\
1 & 2 & 1
\end{bmatrix}
[7]
Here, the kernel differentiates between the top and bottom rows, again with central weighting to focus on immediate vertical variations.[10]
The design of both kernels balances derivative approximation with noise reduction: the differing coefficients (1 and 2) implement a central difference scheme for the primary direction, while the uniform weighting in the perpendicular direction acts as a 1D averaging filter over three pixels, providing smoothing without excessive blurring.[7][10]
For practical implementation, normalization is optional; the original formulation scaled the output by 4 to preserve low-order bits, while many modern implementations divide the kernel outputs by 8 to yield an accurate average gradient estimate, as this matches the sum of the absolute kernel weights.[10][4]
Gradient Calculation
The gradient components G_x and G_y are obtained by convolving the input grayscale image I with the respective horizontal and vertical Sobel kernels. These components approximate the partial derivatives of the image intensity function in the horizontal and vertical directions, respectively, highlighting regions of rapid intensity change corresponding to edges.[11][7]
The magnitude of the gradient |G|, representing the overall edge strength at each pixel, is computed as the Euclidean norm:
|G| = \sqrt{G_x^2 + G_y^2}
This formulation ensures an isotropic response, treating edges equally regardless of orientation, as originally emphasized in the design of the operator.[7][12]
For computational efficiency, particularly in resource-constrained applications, an approximation to the magnitude can be used:
|G| \approx |G_x| + |G_y|
This Manhattan norm variant avoids the square root operation while providing a reasonable estimate of edge strength, though it slightly overestimates the true magnitude.[7]
The direction of the gradient \theta, indicating edge orientation, is calculated using the two-argument arctangent function:
\theta = \atan2(G_y, G_x)
This yields an angle in radians ranging from -\pi to \pi, measured counterclockwise from the positive x-axis, enabling analysis of edge alignment for subsequent processing steps.[11]
A frequent post-processing technique to produce a binary edge map involves applying a threshold T to the magnitude, classifying pixels where |G| > T as edge points while suppressing others; the choice of T depends on the image characteristics and desired sensitivity.[13][14]
Implementation Details
Convolution Mechanism
The convolution mechanism of the Sobel operator applies two distinct 3×3 kernels to a grayscale input image through a process of 2D convolution, yielding approximations of the image gradients in the horizontal and vertical directions.[10]
The input is a grayscale digital image represented as a 2D array of pixel intensities, where each element denotes the brightness value at a specific spatial position, typically ranging from 0 (black) to 255 (white).[15] To compute the gradient components, each kernel is slid across the image in a raster-scan manner, centered successively on every pixel position (i, j). At each such position, the convolution operation calculates a weighted sum by multiplying the kernel's elements with the intensities of the corresponding 3×3 neighborhood of pixels around (i, j) and adding the products together, producing the value G_x(i, j) for the horizontal kernel and G_y(i, j) for the vertical kernel.[15]
Boundaries of the image pose a challenge, as the kernel may overhang when centered near the edges. Common techniques to address this include zero-padding, which extends the image array with a border of zero-valued pixels, or replication, which duplicates the nearest edge pixel values outward; these methods enable full coverage and preserve the output dimensions.[16] Without such handling, the valid convolution positions would be limited to the inner region, resulting in output images reduced by one pixel along each dimension for a 3×3 kernel.[16]
The output comprises two separate gradient images—G_x for horizontal changes and G_y for vertical changes—each matching the input's spatial dimensions when padding is applied. These images encode the local intensity variations that highlight potential edges.[15]
A basic pseudocode implementation of the convolution process, excluding any library optimizations, follows a nested loop structure to iterate over pixel positions and neighborhoods:
function convolve([image](/page/Image), [kernel](/page/Kernel)):
h = [image](/page/Image).[height](/page/Height)
w = [image](/page/Image).width
ks = 3 // kernel size
pad = ks // 2 // 1 for 3x3
output = allocate_array(h, w)
for i from 0 to h-1:
for j from 0 to w-1:
total = 0
for di from -pad to pad:
for dj from -pad to pad:
ni = i + di
nj = j + dj
if 0 <= ni < h and 0 <= nj < w:
total += [image](/page/Image)[ni][nj] * [kernel](/page/Kernel)[pad + di][pad + dj]
else:
total += 0 // zero-padding for boundaries
output[i][j] = total
return output
// Usage for Sobel
G_x = convolve(input_image, horizontal_kernel)
G_y = convolve(input_image, vertical_kernel)
function convolve([image](/page/Image), [kernel](/page/Kernel)):
h = [image](/page/Image).[height](/page/Height)
w = [image](/page/Image).width
ks = 3 // kernel size
pad = ks // 2 // 1 for 3x3
output = allocate_array(h, w)
for i from 0 to h-1:
for j from 0 to w-1:
total = 0
for di from -pad to pad:
for dj from -pad to pad:
ni = i + di
nj = j + dj
if 0 <= ni < h and 0 <= nj < w:
total += [image](/page/Image)[ni][nj] * [kernel](/page/Kernel)[pad + di][pad + dj]
else:
total += 0 // zero-padding for boundaries
output[i][j] = total
return output
// Usage for Sobel
G_x = convolve(input_image, horizontal_kernel)
G_y = convolve(input_image, vertical_kernel)
This structure directly computes the weighted sums at each position while managing boundaries via conditional zero addition.[15]
Computational Aspects
The Sobel operator exhibits linear time complexity of O(N) for processing an N-pixel image, as it applies a fixed-size 3×3 kernel convolution to each pixel independently.[17] This efficiency stems from the constant kernel dimensions, enabling straightforward parallelization across pixels without dependency on image size beyond the linear scaling.[17] Consequently, the operator is well-suited for real-time applications, such as video processing, where frame rates of 30 FPS or higher are achievable on standard hardware.[18]
Implementations often employ integer arithmetic for speed, particularly with 8-bit unsigned input images (CV_8U) to match common grayscale formats and minimize computational overhead.[19] Outputs are typically computed in 16-bit signed integers (CV_16S) to prevent overflow during gradient summation, preserving edge magnitudes without floating-point conversions.[19] For higher accuracy in scenarios requiring sub-pixel precision or handling high-dynamic-range images, floating-point arithmetic (e.g., CV_32F) can be used, though it increases memory usage and processing time.[20]
The operator's design incorporates built-in smoothing via weighted averaging in the kernels, which approximates Gaussian smoothing and reduces sensitivity to Gaussian noise compared to pure differencing methods.[2] However, this smoothing can blur fine edges in low-contrast regions, and for noisy inputs, preprocessing with an additional Gaussian blur is recommended to further suppress artifacts while maintaining edge integrity.[2]
The Sobel operator is readily integrated into major image processing libraries for efficient deployment. In OpenCV, it is implemented via the cv2.Sobel function, supporting customizable kernel sizes and data types for optimized performance.[19] Scikit-image provides the sobel function in its filters module, which computes the gradient magnitude directly on array inputs.[21] MATLAB's edge function defaults to the Sobel method for 2D grayscale edge detection, offering built-in thresholding options.[22]
For large-scale or high-throughput applications, parallelization enhances performance through GPU acceleration. The operator's separability—decomposing the 2D kernel into horizontal and vertical 1D passes—reduces operations from O(9N) to O(6N) per image, facilitating efficient GPU implementations via shader-based convolutions.[17] For very large images, fast Fourier transform (FFT)-based convolution can further accelerate processing by leveraging frequency-domain parallelism, though it is most beneficial when applying multiple filters.[17] Such techniques yield speedups of up to 13× over CPU baselines on consumer GPUs.[18]
Extensions and Variations
Multidimensional Applications
The Sobel operator extends naturally to three-dimensional volumetric data, such as those obtained from MRI scans, by generalizing the 2D kernels into 3×3×3 filters that compute gradients along the x, y, and z axes. These components, denoted as G_x, G_y, and G_z, capture spatial changes in all three directions, enabling edge detection in voxel-based structures like medical imaging volumes. The overall edge magnitude is then derived as G = \sqrt{G_x^2 + G_y^2 + G_z^2}, facilitating applications in 3D reconstruction and segmentation.[23][24]
For color images, the operator is typically applied independently to each RGB channel to compute per-channel gradients, with the final edge strength determined by taking the maximum, average, or vector norm across channels to preserve color-specific edges. Alternatively, converting the image to grayscale or a perceptually uniform space like CIELAB allows for unified processing, where edges are detected primarily on the luminance (L) channel to emphasize intensity boundaries while reducing sensitivity to chromatic noise. This approach balances computational efficiency with the retention of relevant visual information in multichannel data.[25][26]
In video sequences, the Sobel operator incorporates a temporal dimension by treating frames as space-time volumes, applying 3D kernels to detect motion through gradients across spatial and time axes, which aids in applications like object tracking and background subtraction. For general n-dimensional data, such as hyperspectral images or tensor fields, the operator generalizes via recursive convolutions or n-dimensional tensor operations, readily implemented in libraries like TensorFlow for scalable edge detection in high-dimensional arrays.[27][28]
Despite these extensions, multidimensional applications incur significantly higher computational costs due to the increased kernel volume and data dimensionality, scaling linearly with the input size but cubically or higher in practice for 3D and beyond. Mitigation strategies, such as downsampling the input or using separable filters, help reduce this overhead while maintaining edge detection accuracy.[29]
Modified Operators
The Prewitt operator, introduced by Judith M. S. Prewitt in 1970, serves as a foundational adaptation of gradient-based edge detection similar to the Sobel operator but employs uniform integer weights in its 3x3 kernels, resulting in less inherent smoothing along the edge direction compared to Sobel's weighted central column and row.[30] For the horizontal gradient G_x, the kernel is defined as:
\begin{bmatrix}
-1 & 0 & 1 \\
-1 & 0 & 1 \\
-1 & 0 & 1
\end{bmatrix}
while the vertical gradient G_y uses:
\begin{bmatrix}
-1 & -1 & -1 \\
0 & 0 & 0 \\
1 & 1 & 1
\end{bmatrix}.
This design prioritizes simplicity and computational efficiency, making it suitable for applications where moderate noise reduction suffices without the Sobel's diagonal emphasis.[5]
The Roberts cross operator, proposed by Lawrence G. Roberts in 1963 as part of early work on machine perception of three-dimensional solids, utilizes compact 2x2 kernels optimized for detecting diagonal edges, offering faster computation due to its reduced kernel size but exhibiting higher sensitivity to noise owing to minimal smoothing. The kernels are:
G_x = \begin{bmatrix}
0 & 1 \\
-1 & 0
\end{bmatrix}, \quad
G_y = \begin{bmatrix}
1 & 0 \\
0 & -1
\end{bmatrix}.
These kernels approximate the gradient by differencing adjacent diagonal pixels, which enhances responsiveness to 45-degree and 135-degree edges but can produce fragmented results in noisy environments.[5]
To address limitations in rotational invariance of the Sobel and Prewitt operators, the Scharr operator, developed by J. Scharr in 2000, refines the 3x3 kernel design with fractional weights that better approximate isotropic gradient estimation, yielding improved angular response and reduced directional bias across orientations. (Note: Specific kernel matrices involve coefficients like 3/32 and -3/32 for enhanced isotropy, prioritizing empirical optimality over integer simplicity.) This adaptation maintains the 3x3 footprint for compatibility while minimizing systematic errors in gradient magnitude and direction, particularly beneficial for precise edge orientation in subsequent processing tasks.[5]
Integrations of the Sobel operator with second-order derivatives, such as the Laplacian of Gaussian (LoG), extend first-order gradient detection by incorporating zero-crossing analysis for more accurate edge localization, as explored in hybrid approaches that multiply Sobel gradients with Laplacian responses to sharpen edges while suppressing noise through Gaussian smoothing.[31] In this combination, the Sobel kernels first compute directional gradients, and the LoG operator—convolving the image with a Gaussian-smoothed Laplacian—identifies zero-crossings in the second derivative to pinpoint edge positions, effectively blending the strengths of magnitude-based detection with sub-pixel precision for robust edge maps in varied lighting conditions. (Marr and Hildreth's 1980 framework for LoG zero-crossings provides the theoretical basis, adapted here with Sobel for directional enhancement.)
Custom modifications to the Sobel operator often involve adjusting kernel weights to handle anisotropic edges prevalent in textured images, where directional preferences in edge strength require tailored gradient sensitivity to avoid blurring or overemphasis in non-uniform regions. For instance, dynamic anisotropic structures replace symmetric weights with adaptive coefficients based on local texture orientation, enhancing detection of elongated or directionally varying edges while preserving isotropy in homogeneous areas, as demonstrated in noise-resilient implementations that outperform standard Sobel on textured datasets.[32] These adjustments, typically derived from local gradient statistics, enable application-specific tuning without altering the core convolution mechanism.
Practical Usage
Edge Detection Example
To illustrate the application of the Sobel operator in edge detection, consider a simple 5x5 grayscale image depicting a basic vertical step edge with a dark-to-light transition. The pixel intensities are 0 (dark) in the left three columns and 255 (light) in the right two columns, representing an abrupt change in brightness along the boundary between columns 3 and 4. This synthetic image is defined as follows:
0 0 0 255 255
0 0 0 255 255
0 0 0 255 255
0 0 0 255 255
0 0 0 255 255
0 0 0 255 255
0 0 0 255 255
0 0 0 255 255
0 0 0 255 255
0 0 0 255 255
The Sobel operator uses two 3x3 kernels to approximate the gradients: the horizontal gradient kernel G_x for detecting vertical edges and the vertical gradient kernel G_y for detecting horizontal edges. These are given by:
G_x = \begin{bmatrix}
-1 & 0 & 1 \\
-2 & 0 & 2 \\
-1 & 0 & 1
\end{bmatrix}, \quad
G_y = \begin{bmatrix}
-1 & -2 & -1 \\
0 & 0 & 0 \\
1 & 2 & 1
\end{bmatrix}
[33][12]
The computation proceeds by convolving the input image with each kernel separately, assuming zero-padding at the boundaries to maintain the output size at 5x5. The convolution at each position (i, j) yields the partial derivatives G_x(i,j) and G_y(i,j). The gradient magnitude is then calculated as G(i,j) = \sqrt{ G_x(i,j)^2 + G_y(i,j)^2 }, which highlights the strength of edges. Finally, a threshold is applied to G to produce a binary edge map, where pixels exceeding the threshold indicate detected edges.
For a concrete calculation, consider the center position at row 3, column 3 (1-indexed). The 3x3 neighborhood centered there consists of rows 2–4 and columns 2–4:
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
0 0 255
Applying G_x:
- Top row: $0 \cdot (-1) + 0 \cdot 0 + 255 \cdot 1 = 255
- Middle row: $0 \cdot (-2) + 0 \cdot 0 + 255 \cdot 2 = 510
- Bottom row: $0 \cdot (-1) + 0 \cdot 0 + 255 \cdot 1 = 255
Thus, G_x(3,3) = 255 + 510 + 255 = 1020.
Applying G_y:
- Top row: $0 \cdot (-1) + 0 \cdot (-2) + 255 \cdot (-1) = -255
- Middle row: $0 \cdot 0 + 0 \cdot 0 + 255 \cdot 0 = 0
- Bottom row: $0 \cdot 1 + 0 \cdot 2 + 255 \cdot 1 = 255
Thus, G_y(3,3) = -255 + 0 + 255 = 0.
The magnitude is G(3,3) = \sqrt{1020^2 + 0^2} = 1020. Computations at adjacent positions along the step boundary (columns 3–5) yield high magnitudes (765–1082) due to the kernel's response and zero-padding at the right edge, while positions away from the transition (e.g., column 1–2) yield near-zero values. Applying a threshold of 500 to the full magnitude map binarizes it, marking a band of detected edges.
The resulting edge map, affected by boundary padding, is:
0 0 1 1 1
0 0 1 1 1
0 0 1 1 1
0 0 1 1 1
0 0 1 1 1
0 0 1 1 1
0 0 1 1 1
0 0 1 1 1
0 0 1 1 1
0 0 1 1 1
(1 indicates edge pixel, 0 indicates non-edge.) Note that zero-padding introduces a false response in column 5, spreading the detection beyond the true step edge between columns 3 and 4; replicate padding or larger images can mitigate such artifacts.
In this example, the gradient magnitudes peak at the intensity transition and extend due to the operator's smoothing, demonstrating how the Sobel operator approximates the image derivative to isolate boundaries, though boundary conditions affect results near edges. A simple text-based diagram of the process is:
Input Step Edge → Convolution with G_x (high response) + G_y (low response) → Magnitude Map (peak at edge) → Threshold → Binary Edge Band (with boundary artifacts).
To demonstrate common pitfalls, consider adding Gaussian noise (e.g., small random variations of ±10 to some pixels) to the input image. This introduces spurious low-magnitude gradients throughout, creating faint artifacts in the magnitude map that may produce false edges after thresholding if the threshold is too low. Additionally, zero-padding at the image boundaries can distort gradient estimates near the edges, leading to weakened, shifted, or extraneous detections at the top, bottom, left, and right borders.
[34][35]
The Sobel operator is commonly evaluated against other edge detection techniques using key performance metrics, including edge localization error (measuring how precisely edges align with true boundaries), noise suppression via Peak Signal-to-Noise Ratio (PSNR, where higher values indicate better preservation of signal quality amid noise, computed on edge maps vs. ground truth), and runtime efficiency on benchmark images such as the Lena test image. These metrics highlight trade-offs in accuracy, robustness to noise, and computational cost, with evaluations often conducted on grayscale images under varying noise levels (e.g., Gaussian or salt-and-pepper). For instance, runtime is typically measured in seconds on standard hardware from older benchmarks, while PSNR quantifies how well the detected edge map retains fidelity compared to ground-truth edges.[36][37]
Compared to the Canny operator, the Sobel operator offers simplicity and low computational cost but generally lags in accuracy for thin edge localization, as Canny's multi-stage pipeline—including Gaussian smoothing for noise reduction and non-maximum suppression—yields cleaner, more precise edges. On noisy images, performance varies by implementation; some benchmarks show Sobel with slightly higher PSNR (e.g., ~11 dB vs. ~10 dB for Canny), though Canny reduces false positives through better noise handling, at the cost of increased complexity (runtimes similar or 1.5–2 times longer in various tests).[38][37]
Relative to the Prewitt and Roberts operators, the Sobel operator provides enhanced noise suppression thanks to its weighted kernel centers (emphasizing the 2 in the 1-2-1 averaging), resulting in fewer artifacts than Prewitt's uniform weighting or Roberts' compact 2x2 cross-gradient approach, which is highly sensitive to noise. The Roberts operator offers the fastest runtime—often under that of Sobel—making it suitable for binary or low-noise scenarios, while Sobel balances speed and stability better for general grayscale images. Quantitative studies show Sobel yielding lower edge localization errors than Roberts but comparable to Prewitt in many cases.[39][40]
| Operator | Speed (Relative Runtime) | Noise Suppression (Typical PSNR on Edge Maps) | Edge Localization |
|---|
| Sobel | High (e.g., ~1 s on benchmarks) | Medium (~10-11 dB) | Medium |
| Canny | Medium (similar to ~1.5x Sobel) | Medium-High (~10 dB) | High |
| Prewitt | High (similar to Sobel) | Low (~9-11 dB) | Medium |
| Roberts | Very High (faster than Sobel) | Low (~9-10 dB) | Low |