Is this good for educators?

Yes — this version of "Image Processing Chain for a sampling error" is framed for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs.

Do I need to install anything?

No. It runs in any modern browser, free, with no account required.

For Educators · Image Processing Chain

Image Processing Chain for a sampling error

Built for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs. Simulate a sampling error live below — adjust the inputs and watch it respond, right in your browser.

Convolution

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Image Convolution / KernelsLive

the operation inside every CNN

Controls

Presets

Smoothing sigma

Kernel width (taps)

3×3 kernel

-1

Each output pixel is a weighted sum of its 3×3 neighborhood. Slide this kernel over an image and you get blur, sharpening, or edge maps — the exact operation a convolutional neural network learns.

▶ Run in Python

Data Inspector

KernelEdge detect

Window3×3

Smoothing sigma0.4

Sum0.00

Governing equation

(f * g) (t) = \int_{- \infty}^{\infty} f (τ) g (t - τ) d τ (σ = 0.4, taps = 3)

Reading this result: Convolution slides the 3-tap kernel over the image; each output pixel is a weighted sum of its 3×3 neighbors. At sigma ≈ 0.4 the weights stay tightly concentrated, so edges and fine detail survive and high-frequency structure is preserved.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

↓

FFT

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Fourier Transform (FFT)Live

decompose a signal into frequencies

Controls

Build a signal from a few sine tones plus noise, and the discrete Fourier transform recovers exactly which frequencies are present — the foundation of all signal processing.

Presets

Tone 1 frequency

Tone 2 frequency

Tone 3 frequency

Noise

▶ Run in Python

Data Inspector

Samples256

Peaks at4, 9

TransformDFT

Governing equation

X_{k} = n = 0 \sum N - 1 x_{n} e^{- 2 π ik n / N} (N = 256, O (N lo g N))

Reading this result: The FFT splits this 2-tone signal into bins: each input sinusoid at 4, 9 appears as a distinct spike, and with noise at 0 the baseline between them stays clean.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

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Frequently asked questions

Is this good for educators?: Yes — this version of "Image Processing Chain for a sampling error" is framed for educators teaching it to a class. Drop a live demo into a lecture or assign it as a shareable link — no lab installs.
Do I need to install anything?: No. It runs in any modern browser, free, with no account required.