Deterministic images with known properties for regression-testing resize, crop, convert, and filter pipelines.
A colour fruit still life (apple, orange, banana, grapes), paired with a true-greyscale conversion — a real-world subject for testing desaturation and how distinct hues collapse to similar greys.
True 8-bit greyscale conversion of the fruit still life, the paired reference for testing colour-to-grey conversion.
A smooth three-channel gradient composition, paired with its greyscale conversion to test luminance handling.
True 8-bit greyscale conversion of the gradient composition, the paired reference for testing colour-to-grey conversion.
Vertical bars sweeping the full hue circle at maximum saturation, paired with a greyscale version that shows how colours of different hues collapse to similar greys.
True 8-bit greyscale conversion of the full-saturation hue bars, the paired reference for testing colour-to-grey conversion.
A 16-step greyscale wedge from pure black to pure white, for testing tone reproduction, banding, and monitor calibration.
A flat 50% grey card (rgb 128,128,128), a reference for white balance and exposure testing.
A pure black-and-white checkerboard, useful for testing scaling, aliasing, and edge handling.
The clean reference image for denoise base A — a fruit still life on studio white. Diff the noisy variants in this set against this file to measure filter accuracy.
Base A with additive Gaussian noise at σ=10 (seed 4210). Compare against the clean reference in this set to score a denoise filter.
Base A with additive Gaussian noise at σ=25 (seed 4225). Compare against the clean reference in this set to score a denoise filter.
Base A with additive Gaussian noise at σ=50 (seed 4250). Compare against the clean reference in this set to score a denoise filter.
Base A with 2% salt-and-pepper noise (seed 42002). A direct fixture for testing median and morphological denoisers against the clean reference.
Base A with 5% salt-and-pepper noise (seed 42005). A direct fixture for testing median and morphological denoisers against the clean reference.
Base A with 10% salt-and-pepper noise (seed 42010). A direct fixture for testing median and morphological denoisers against the clean reference.
The clean reference image for denoise base B — a coffee mug on studio white. Diff the noisy variants in this set against this file to measure filter accuracy.
Base B with additive Gaussian noise at σ=10 (seed 7710). Compare against the clean reference in this set to score a denoise filter.
Base B with additive Gaussian noise at σ=25 (seed 7725). Compare against the clean reference in this set to score a denoise filter.
Base B with additive Gaussian noise at σ=50 (seed 7750). Compare against the clean reference in this set to score a denoise filter.
Base B with 2% salt-and-pepper noise (seed 77002). A direct fixture for testing median and morphological denoisers against the clean reference.
Base B with 5% salt-and-pepper noise (seed 77005). A direct fixture for testing median and morphological denoisers against the clean reference.
Base B with 10% salt-and-pepper noise (seed 77010). A direct fixture for testing median and morphological denoisers against the clean reference.
A coffee mug photographed on a clean studio-white background — the easy input for a background remover, paired 1:1 with its transparent cut-out for scoring.
The expected transparent cut-out of the coffee mug, paired with the on-background version — the ground truth for a background-removal test.
A left-to-right alpha gradient over a solid colour, for testing how a viewer or compositor renders partial transparency.
A 1-pixel-wide, 5000-pixel-tall strip — an extreme aspect ratio for stress-testing layout, scaling, and thumbnail generators.
An 8-frame looping animated GIF of a moving dot — tests animation handling, first-frame extraction, and GIF parsing.
The fruit still life saved as a JPEG with an embedded sRGB ICC colour profile — paired with an untagged copy for testing colour-management and profile handling.
The same photo saved with no embedded ICC profile — the untagged twin of the sRGB-tagged JPEG, for testing how a tool assumes/handles a missing colour profile.
The fruit still life as a baseline (sequential) JPEG — paired with a progressive JPEG of identical content for testing decode order and progressive-rendering support.
The same photo as a progressive JPEG (loads coarse-to-fine) — the twin of the baseline JPEG, for testing progressive decoding and byte-order handling.
A 16-bit (deep-colour) grayscale PNG holding a smooth 0–65535 gradient — for testing high-bit-depth support and spotting banding when a tool truncates to 8-bit.
A 256×256 checkerboard texture that tiles seamlessly — for testing texture mapping, pattern fills, tiling, and background rendering.
A 256×256 polka dots texture that tiles seamlessly — for testing texture mapping, pattern fills, tiling, and background rendering.
A 256×256 grid texture that tiles seamlessly — for testing texture mapping, pattern fills, tiling, and background rendering.
A 256×256 diagonal stripes texture that tiles seamlessly — for testing texture mapping, pattern fills, tiling, and background rendering.
A 256×256 grain texture — for testing texture mapping, pattern fills, tiling, and background rendering.
A 256×256 identicon-style avatar generated deterministically from the seed “ada-lovelace” — a symmetric 5×5 block pattern like the default avatars on many sites. For testing avatar rendering, placeholder generation, and image pipelines.
A 256×256 identicon-style avatar generated deterministically from the seed “grace-hopper” — a symmetric 5×5 block pattern like the default avatars on many sites. For testing avatar rendering, placeholder generation, and image pipelines.
A 256×256 identicon-style avatar generated deterministically from the seed “alan-turing” — a symmetric 5×5 block pattern like the default avatars on many sites. For testing avatar rendering, placeholder generation, and image pipelines.
A 256×256 identicon-style avatar generated deterministically from the seed “katherine-johnson” — a symmetric 5×5 block pattern like the default avatars on many sites. For testing avatar rendering, placeholder generation, and image pipelines.
Clean reference for the crease photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-crease.
Damaged input for crease restoration testing (seed 408). Pair with the clean reference and damage mask in this group.
Clean reference for the scratch photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-scratch.
Damaged input for scratch restoration testing (seed 409). Pair with the clean reference and damage mask in this group.
Clean reference for the dust photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-dust.
Damaged input for dust restoration testing (seed 410). Pair with the clean reference and damage mask in this group.
Clean reference for the water-stain photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-water-stain.
Damaged input for water-stain restoration testing (seed 411). Pair with the clean reference and damage mask in this group.
Clean reference for the torn-corner photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-torn-corner.
Damaged input for torn-corner restoration testing (seed 412). Pair with the clean reference and damage mask in this group.
Clean reference for the horizontal-cut photo-restoration case. Diff a restorer's output against this file; the damaged input and mask share group img-restore-horizontal-cut.
Damaged input for horizontal-cut restoration testing (seed 413). Pair with the clean reference and damage mask in this group.
Clean reference for deblur base A. Compare deblurred outputs of the blurred variants in this group against this file.
Base A with gauss-3 blur. Score a deblur tool against the clean reference in this group.
Base A with gauss-6 blur. Score a deblur tool against the clean reference in this group.
Base A with motion-h blur. Score a deblur tool against the clean reference in this group.
Base A with motion-v blur. Score a deblur tool against the clean reference in this group.
Base A with motion-diag blur. Score a deblur tool against the clean reference in this group.
Base A with shake blur. Score a deblur tool against the clean reference in this group.
Clean reference for deblur base B. Compare deblurred outputs of the blurred variants in this group against this file.
Base B with gauss-3 blur. Score a deblur tool against the clean reference in this group.
Base B with gauss-6 blur. Score a deblur tool against the clean reference in this group.
Base B with motion-h blur. Score a deblur tool against the clean reference in this group.
Base B with motion-v blur. Score a deblur tool against the clean reference in this group.
Base B with motion-diag blur. Score a deblur tool against the clean reference in this group.
Base B with shake blur. Score a deblur tool against the clean reference in this group.
True-colour ground truth for the fruit colourization suite. Compare a colourizer's output against this reference to score accuracy.
Greyscale input for colourizing the fruit scene. Pair with the colour ground truth in this group.
Sepia input for colourizing the fruit scene. Pair with the colour ground truth in this group.
True-colour ground truth for the mug colourization suite. Compare a colourizer's output against this reference to score accuracy.
Greyscale input for colourizing the mug scene. Pair with the colour ground truth in this group.
Sepia input for colourizing the mug scene. Pair with the colour ground truth in this group.
True-colour ground truth for the plant colourization suite. Compare a colourizer's output against this reference to score accuracy.
Greyscale input for colourizing the plant scene. Pair with the colour ground truth in this group.
Sepia input for colourizing the plant scene. Pair with the colour ground truth in this group.
Clean synthetic face illustration (not a real person) used as ground truth for face-restore / enhancement tools.
Deliberately degraded synthetic face (jpeg) for testing face restore / enhancement against the clean reference.
Damage-region mask (L) for the face-restore case — white = degraded region.
Clean synthetic face illustration (not a real person) used as ground truth for face-restore / enhancement tools.
Deliberately degraded synthetic face (blur) for testing face restore / enhancement against the clean reference.
Damage-region mask (L) for the face-restore case — white = degraded region.
Clean synthetic face illustration (not a real person) used as ground truth for face-restore / enhancement tools.
Deliberately degraded synthetic face (down) for testing face restore / enhancement against the clean reference.
Damage-region mask (L) for the face-restore case — white = degraded region.
Clean synthetic face illustration (not a real person) used as ground truth for face-restore / enhancement tools.
Deliberately degraded synthetic face (combo) for testing face restore / enhancement against the clean reference.
Damage-region mask (L) for the face-restore case — white = degraded region.
Clean fruit still life without marks — ground truth for synthetic SAMPLE watermark removal tests.
Fruit still life with a synthetic SAMPLE watermark (not a real brand). Use with the clean reference and mask to score removers.
Mask of the synthetic SAMPLE watermark region for supervised removal tests.
Clean fruit still life without marks — ground truth for synthetic SAMPLE watermark removal tests.
Fruit still life with a synthetic SAMPLE watermark (not a real brand). Use with the clean reference and mask to score removers.
Mask of the synthetic SAMPLE watermark region for supervised removal tests.
Clean fruit still life without marks — ground truth for synthetic SAMPLE watermark removal tests.
Fruit still life with a synthetic SAMPLE watermark (not a real brand). Use with the clean reference and mask to score removers.
Mask of the synthetic SAMPLE watermark region for supervised removal tests.
Clean fruit still life without marks — ground truth for synthetic SAMPLE watermark removal tests.
Fruit still life with a synthetic SAMPLE watermark (not a real brand). Use with the clean reference and mask to score removers.
Mask of the synthetic SAMPLE watermark region for supervised removal tests.
Clean fruit still life without marks — ground truth for synthetic SAMPLE watermark removal tests.
Fruit still life with a synthetic SAMPLE watermark (not a real brand). Use with the clean reference and mask to score removers.
Mask of the synthetic SAMPLE watermark region for supervised removal tests.
Full-frame plant scene used as the target for outpainting tests with a 64px empty border input.
Plant content centred with a 64px empty border — classic outpainting / generative expand fixture.
White = region to outpaint / expand; black = known content.
Full-frame plant scene used as the target for outpainting tests with a 128px empty border input.
Plant content centred with a 128px empty border — classic outpainting / generative expand fixture.
White = region to outpaint / expand; black = known content.
Full-frame reference for 96px border outpainting.
Content with 96px empty border for outpainting tools.
Expand-region mask for outpainting.
Full-frame reference for 192px border outpainting.
Content with 192px empty border for outpainting tools.
Expand-region mask for outpainting.
Fruit still-life content photo for neural / filter style-transfer tests. Pair with the style reference images in this group.
Style reference (posterize) derived from the same scene — use as a target style when transferring the photo input.
Style reference (line-art) derived from the same scene — use as a target style when transferring the photo input.
Style reference (paint-like) derived from the same scene — use as a target style when transferring the photo input.
Style reference (comic) derived from the same scene — use as a target style when transferring the photo input.
Abstract geometric style plate for style-transfer tools that accept a separate style image.
Correctly exposed mug reference for scoring auto-enhance, exposure correction, and dehaze tools.
Mug photo with deliberate underexposed for testing auto-enhance and colour/exposure correction against the clean reference.
Mug photo with deliberate overexposed for testing auto-enhance and colour/exposure correction against the clean reference.
Mug photo with deliberate low contrast for testing auto-enhance and colour/exposure correction against the clean reference.
Mug photo with deliberate high contrast for testing auto-enhance and colour/exposure correction against the clean reference.
Mug photo with deliberate warm cast for testing auto-enhance and colour/exposure correction against the clean reference.
Mug photo with deliberate cool cast for testing auto-enhance and colour/exposure correction against the clean reference.
Mug photo with deliberate haze for testing auto-enhance and colour/exposure correction against the clean reference.
Clean fruit still life for a second enhancement ladder subject.
Severely underexposed fruit scene for enhancement / brighten tools.
Hazy fruit scene for dehaze / clarity enhancement tests.
Lossless PNG ground truth for JPEG blocking / ringing artefact repair tests.
Fruit still life saved at JPEG quality 5 — heavy blocking/ringing for artefact-removal and enhance tools.
Fruit still life saved at JPEG quality 10 — heavy blocking/ringing for artefact-removal and enhance tools.
Fruit still life saved at JPEG quality 15 — heavy blocking/ringing for artefact-removal and enhance tools.
Fruit still life saved at JPEG quality 25 — heavy blocking/ringing for artefact-removal and enhance tools.
Fruit still life saved at JPEG quality 40 — heavy blocking/ringing for artefact-removal and enhance tools.
A linear 16-bit greyscale TIFF ramp (0–65535). Not a PQ HDR master — for testing high bit-depth TIFF loaders and 16→8 truncation.
A 16-bit TIFF biased into the upper code values (near clip) — for highlight-rolloff and tone-map tests on linear data.
64×64 linear 16-bit greyscale TIFF with deflate — Wave H depth for high bit-depth loaders.
64×64 linear 16-bit checkerboard TIFF for bit-depth and downsample tests.
Linear 16-bit TIFF with a dark edge and near-clip field — tone-map edge case.
Colour ground-truth for the mug conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Tangent-space surface-normal map encoded from the scene depth — a normal-conditioning input for guided generation and relighting tests.
Semantic segmentation map (subject vs background) — the label reference for segmentation-conditioned generation and mask-quality scoring.
Sparse scribble derived from the edges — the loose hand-drawn control input for scribble-guided image generation.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
Colour ground-truth for the apple conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Tangent-space surface-normal map encoded from the scene depth — a normal-conditioning input for guided generation and relighting tests.
Semantic segmentation map (subject vs background) — the label reference for segmentation-conditioned generation and mask-quality scoring.
Sparse scribble derived from the edges — the loose hand-drawn control input for scribble-guided image generation.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
Colour ground-truth for the plant conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Tangent-space surface-normal map encoded from the scene depth — a normal-conditioning input for guided generation and relighting tests.
Semantic segmentation map (subject vs background) — the label reference for segmentation-conditioned generation and mask-quality scoring.
Sparse scribble derived from the edges — the loose hand-drawn control input for scribble-guided image generation.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
Colour ground-truth for the robot conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Tangent-space surface-normal map encoded from the scene depth — a normal-conditioning input for guided generation and relighting tests.
Semantic segmentation map (subject vs background) — the label reference for segmentation-conditioned generation and mask-quality scoring.
Sparse scribble derived from the edges — the loose hand-drawn control input for scribble-guided image generation.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
OpenPose-style keypoint skeleton for the figure — a pose-control input for guided generation and a target for pose-estimation checks.
Colour ground-truth for the cat conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Tangent-space surface-normal map encoded from the scene depth — a normal-conditioning input for guided generation and relighting tests.
Semantic segmentation map (subject vs background) — the label reference for segmentation-conditioned generation and mask-quality scoring.
Sparse scribble derived from the edges — the loose hand-drawn control input for scribble-guided image generation.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
OpenPose-style keypoint skeleton for the figure — a pose-control input for guided generation and a target for pose-estimation checks.
Colour ground-truth for the fruit conditioning suite. Every map in this group is derived from this exact image, so a guided model's output can be scored against it.
Binary Canny edge map extracted from the ground truth — feed as a structure-control input and score the generated result against the reference.
Synthetic depth map (near = bright) for depth-conditioned generation and monocular depth-estimation regression against a known reference.
Tangent-space surface-normal map encoded from the scene depth — a normal-conditioning input for guided generation and relighting tests.
Semantic segmentation map (subject vs background) — the label reference for segmentation-conditioned generation and mask-quality scoring.
Sparse scribble derived from the edges — the loose hand-drawn control input for scribble-guided image generation.
Clean line-art extraction (black lines on white) for line-art-conditioned generation and line-extraction quality tests.
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