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Computational Photography

A smartphone camera takes 9 bracketed exposures in 100 ms, aligns them sub-pixel, HDR-merges and applies a neural tone-mapper — all before your thumb leaves the shutter. Every step is physics.

7 simulations Optics · Sensors Signal Processing · AI Pipeline

Simulations

Open any simulation — runs instantly in your browser

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Popular★★☆ Moderate
Diffraction & Airy Disk — Lens Sharpness
Circular aperture diffraction produces an Airy disk whose radius sets the absolute resolution limit. Sweep f-number to find the optimal aperture balancing diffraction against aberrations.
DiffractionAiryAperture
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★★☆ Moderate
Fourier Optics — MTF & Spatial Frequency
Decompose a scene into spatial frequencies — the lens acts as a low-pass filter described by its Modulation Transfer Function. Build intuition for RAW sharpening and demosaicing artefacts.
FourierMTFSpatial Freq
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★★☆ Moderate
Lens Flare & Chromatic Aberration
Ray-traced Snell-law refraction through a multi-group lens system — lateral chromatic aberration, barrel/pincushion distortion and ghost reflections across air-glass interfaces.
Ray TraceAberrationSnell
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★☆☆ Beginner
Bayer Pattern & Demosaicing
A 64×64 colour scene rendered through a Bayer RGGB mask shows the raw mosaic. Toggle between bilinear, AHD and LMMSE demosaicing — spot colour fringing and zipper artefacts.
BayerDemosaicRAW
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★★☆ Moderate
Night Photography — Shot Noise & Stacking
Photon shot noise and read noise scale differently with ISO — stacking N frames reduces noise by √N. Watch a faint galaxy emerge from noise as you add frames, exactly as astrophotographers do.
PoissonStackingSNR
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★★☆ Moderate
HDR Merge & Tone-Mapping
Three exposures (−2, 0, +2 EV) merge into an HDR image — apply Reinhard, ACES filmic and exposure-based tone-mapping operators. See why the same scene looks different under each curve.
HDRReinhardACES
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★★★ AdvancedNew
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New ★★☆ Moderate
Image Filters
Image processing with convolution, Sobel edge detection, Gaussian blur and FFT filtering.
image processing convolution Sobel
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New ★★☆ Moderate
HDR Tonemapping Operators
High Dynamic Range images need tonemapping to display on LDR screens. Compare Reinhard, Filmic ACES,...
HDR Tonemapping ACES
Super-Resolution — Drizzle Algorithm
Sub-pixel-shifted frames are combined by inverse-distance drizzle weighting to recover detail below the pixel-pitch limit. The same algorithm used by Hubble and James Webb for finest images.
DrizzleSuper-ResSub-pixel

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About Computational Photography Simulations

Convolution filters, HDR, depth-of-field, and image processing — visualised

Computational photography simulations demonstrate the algorithmic image processing that underlies modern camera software. Convolution filter visualisers apply Gaussian blur, edge-detection (Sobel, Canny), unsharp mask, and emboss kernels to live or uploaded photographs, showing the spatial-frequency effect of each filter in real time alongside its Fourier-domain representation.

HDR tone-mapping simulations take a set of exposure-bracketed frames, merge them into a high-dynamic-range radiance map, and apply Reinhard and filmic tone-mapping operators to produce a displayable image. Depth-of-field ray-tracing models distribute rays across a virtual aperture disc and sum the bokeh blur for out-of-focus scene elements. These techniques are the core of smartphone computational cameras, medical image enhancement, and autonomous-vehicle perception systems.

Each simulation in this category is built with accuracy and interactivity in mind. The underlying mathematical models are the same ones used in academic research and professional engineering — just made accessible through a web browser. Changing parameters in real time and observing the results is one of the most effective ways to build intuition for complex scientific and engineering concepts.

Key Concepts

Topics and algorithms you'll explore in this category

Camera Sensor PhysicsShot noise, read noise, and QE spectral response
RAW Processing PipelineDemosaicing, white balance, tone mapping
Diffraction LimitAiry disk radius and optimal f-stop
HDR ImagingExposure fusion and Reinhard tone mapping
Depth of FieldCircle of confusion, hyperfocal distance, bokeh
Image DenoisingBM3D, wavelet thresholding, and NLM filtering

Frequently Asked Questions

Common questions about this simulation category

What computational photography topics are covered?
Camera sensor noise models, RAW processing (demosaicing, white balance, tone curves), diffraction limits, HDR exposure fusion, depth-of-field optics, and image denoising algorithms.
What is the diffraction limit simulation about?
It computes Airy disk radius as a function of f-stop and wavelength, showing the optimal aperture that balances diffraction softening against geometric aberration sharpness for a given sensor pixel pitch.
Can photographers use these simulations?
Yes — they are designed to give photographers an intuitive understanding of the physics behind their camera settings, from optimal aperture choice to the noise floor of their sensor.

Every Computational Photography simulation here runs free in your browser, letting you experiment with each interactive Computational Photography model — HDR tone mapping and exposure fusion — without installing anything. Adjust exposure brackets, tone-curve parameters and local contrast settings to observe real-time image transformations and learn Computational Photography online at your own pace, whether you are a photographer, an image-processing student or a computer vision researcher. Computational photography sits at the intersection of optics, signal processing and machine learning: the same algorithms that merge multiple exposures to recover shadow and highlight detail also power the night modes on every modern smartphone, the focus stacking used in macro and scientific imaging, and the deep-learning denoisers that are replacing traditional RAW converters across the photography industry.