# Foveon – Bayer to Foveon X3, learned, Mac App using deep learning > Source: > Published: 2026-06-28 04:27:03+00:00 # Foveon A neural sensor translator. Takes a photo from any Bayer-array camera and renders it as if it were shot on a Sigma DP2 Merrill — the **Foveon X3** stacked-sensor look, with the colour and microdetail Foveon is famous for, on hardware you already own. Under the hood: a modified *U-Net* with an extra layer injected between the encoder bottleneck and the upsampling decoder. The injected channel carries a one-dimensional encoding of *three-layer pixel-stack* structure — the B·G·R photodiode column that a Foveon sensor captures and a Bayer sensor can’t. Trained end-to-end against matched Bayer → Merrill scene pairs. with 3-layer pixel injection at bottleneck Foveon X3 stacked- sensor look out scene pairs against Sigma DP2 Merrill ## What it is Most digital cameras capture colour through a **Bayer colour filter array**: each photosite sees only one of R, G, or B, and the other two channels are interpolated from the neighbours (demosaiced). It’s efficient, but it costs you. The interpolation introduces colour fringing on sharp edges, smears fine detail, and produces the “digital” micro-contrast that even high-end Bayer cameras can’t fully shake. The **Foveon X3 sensor** — Sigma’s now-rare design used in the DP1, DP2, and DP3 Merrill cameras — works the way colour film does. Three photodiode layers are stacked vertically at every single pixel position. The top layer absorbs blue, the middle layer green, the bottom layer red. Every pixel captures the full colour. No interpolation, no demosaicing artefacts, no false detail. The result is the “Foveon look”: extraordinary microdetail and a particular colour rendition — warm, dimensional, almost slide-film — that people build entire camera systems around. **Foveon** (the app) is a neural network that learns the mapping between the two. Feed it a JPEG or RAW from a normal Bayer camera (phone, mirrorless, DSLR) and it predicts what the same scene would look like shot on a Foveon X3 sensor. Geometry stays the same; colour, tonality, and micro-detail rendering shift toward the Merrill side of the training distribution. ## Bayer vs Foveon — the structural problem ## The architecture The core is a standard convolutional **U-Net**: an encoder that downsamples the input image into a compact feature bottleneck, paired with a decoder that upsamples back to full resolution, with *skip connections* at every level so fine spatial detail survives the trip through the bottleneck. The modification is a single new layer dropped in between the encoder’s final downsampling block and the decoder’s first upsampling block: a **1D pixel-stack injection layer** that concatenates a one-dimensional encoding of how colour absorbs through silicon depth on a real Foveon sensor — blue first, then green, then red. The decoder learns to use this prior to reconstruct the kind of inter-channel coupling that real X3 captures exhibit — chroma that’s registered with luminance instead of interpolated against it. *order*in which colour absorbs through silicon depth on a real Foveon sensor — blue at the top, green in the middle, red at the bottom. That ordering is a 1D signal per pixel column, not a 2D spatial structure. Keeping it 1D keeps the parameter count low and forces the network to learn the inter-channel coupling rather than memorise a per-region lookup. ## Training The model is trained end-to-end against matched scene pairs: the same subject captured simultaneously by a Bayer-sensor camera and a Sigma DP2 Merrill, the two images aligned and cropped to identical framing. The Bayer image is the network input; the Merrill image is the reconstruction target. ### Capture matched pairs A Bayer-sensor camera and a Sigma DP2 Merrill are tripod-mounted side-by-side. Each scene is photographed simultaneously by both. We bias capture toward the categories where Foveon rendering differs most visibly from Bayer: outdoor light, foliage, skin tones, fabric, water, metallic surfaces. ### Align & crop For each pair we run a homography-based alignment (feature-matched on luminance) so the two images share an identical pixel grid despite the different sensor sizes, focal lengths, and viewpoints. The aligned tiles are cropped to common framing and resampled to a shared resolution. Misaligned pairs are filtered out. ### Develop the Merrill target DP2 Merrill RAW (.X3F) files are developed in Sigma Photo Pro with neutral parameters — zero added sharpening, native colour profile, no creative tone curve. This is the “ground truth” the network is asked to predict: what the X3 sensor saw, minimally interpreted. ### Tile & augment Each aligned pair is sliced into overlapping 256×256 tiles for training (the U-Net handles arbitrary input sizes at inference, but training on fixed tiles keeps batches efficient). Augmentations: random crop, horizontal flip, small rotation, exposure jitter on both halves of the pair in lock-step. ### Loss composition Three components, summed with weights tuned to keep colour and detail in balance: `L1` pixel loss anchors absolute colour fidelity; `VGG perceptual` loss carries microdetail and texture (the network learns the Merrill *look*, not just the average pixel value); a small `TV` smoothness penalty keeps the output from inventing sharpening artefacts in flat regions. ### Train & validate AdamW optimiser at `lr = 2e-4` , cosine schedule, mixed-precision on a single 24 GB GPU. Batch size 16 tiles. We hold out roughly 5% of pairs as a validation set stratified by category (skin, foliage, fabric, etc.) so the model isn’t accidentally memorising a few scenes. Best checkpoint is the one that minimises validation perceptual loss, not L1 — perceptual loss tracks the X3 look more reliably than pixel error. ## What it produces A schematic comparison: what the same uniform skin patch looks like coming out of a Bayer demosaic pipeline (left) versus what the network predicts the Merrill would have rendered (right). The Bayer side has slight chroma drift between cells; the Merrill side is uniform. ## The app Foveon ships as a native macOS app with a clean three-pane workflow: pick a photo on the left, watch the preview update on the right. Three sliders shape the conversion: `0` the output sits closer to the original Bayer colours; at `1.0` (default) the full Merrill warmth and saturation come through. `0.18` matches Sigma Photo Pro’s neutral tone; push it for a more cinematic look, drop it for a flatter starting point you can grade externally. Drag either preview panel to pan; both panels move together so the same crop is always in view on both sides. `Reset` snaps the view back to fit, `Save Foveon Photo…` writes the result. ## Install & use Foveon runs natively on Apple Silicon. A 24-megapixel input converts in well under a second on an M2; the model uses MPS for inference and stays within ~3 GB of unified memory.