Enhance an image 2×
Drop one image here, or paste an image with Ctrl/Cmd+V.
One static JPEG / PNG / WebP · 15 MB · 262144 input pixels · 1024px per side
Choose an image to begin.
Neural luminance enhancement may create artifacts or false detail. Check the actual result at 100%.
Images stay on this device. Selecting an image starts processing and loads model assets from this site.
Help & FAQs
Choose once; switch tools freely.
Double the image dimensions with local luminance super-resolution, then inspect the actual result.
How to enhance an image
- Choose one static JPEG, PNG or WebP: 15 MB, 262144 input pixels and 1024 pixels per side maximum.
- Enhancement starts after image validation. The local model processes overlapping tiles; Cancel remains available with progress.
- Inspect the actual result at 100% or 200%, download PNG, or explicitly apply it to another image tool.
Local model, transparent output and limits
The fixed ESPCN 2× weights and task-owned WebGL2 runtime load from this site after image selection and validation. No image is uploaded. Width and height each double after native orientation decoding. The tool re-encodes PNG and keeps interpolated transparency; source metadata is not copied.
Tiles include the model’s four-pixel receptive-field context and use original image boundaries. Actual tile outputs and the full-model output are independently compared. The model predicts full-range luminance; original chroma and premultiplied alpha are interpolated. Color and fine details can still change.
Replacing, clearing, cancelling or leaving invalidates old outputs, late callbacks and download scopes. Bitmap, canvas, GPU resources and object URLs are released. Images, names, dimensions, processing progress and arbitrary exception text do not enter analytics. Processing may reach its budget on slower devices; try a smaller input.
Common questions
Is this ordinary resizing?
The ESPCN 2× trained model predicts four luminance samples for each input pixel. It is actual neural inference rather than ordinary canvas resizing or sharpening. Chroma and alpha use bilinear interpolation, so not every channel is model-generated.
Will it recover true missing details?
No. A compact model can change edges and produce false detail or artifacts, especially in text, faces and severely compressed images. The output is exactly twice the width and height, but this does not guarantee higher fidelity. Compare the actual result at 100% before using it.
Why are input images limited?
Inference uses bounded tiles with a four-pixel context, a maximum of 262144 input pixels and 45-second checkpoints. Output has at most 1048576 pixels. This keeps the local browser path bounded; incompatible GPUs fail explicitly without a fake enhanced result.