Drizzle (image processing)

Drizzling is an image processing technique used primarily in astronomy to improve the resolution and signal-to-noise ratio of images, particularly those obtained from undersampled data. The term "drizzle" originates from its initial implementation in the Hubble Space Telescope (HST) data processing pipelines.

The core concept behind drizzling involves mapping each pixel in an input image onto a smaller area in the output image. This allows for the finer sampling of the image data, effectively recovering information lost due to undersampling. Undersampling occurs when the pixel size of the detector is larger than the spatial resolution of the telescope or instrument, leading to information being blurred together within a single pixel.

The drizzling process generally involves the following steps:

1. Image Alignment: Multiple images of the same astronomical object are obtained, and these images are carefully aligned with respect to each other. Accurate alignment is crucial for successful drizzling.

2. Distortion Correction: Any optical distortions present in the images are corrected. This can involve complex mathematical models to accurately map the positions of pixels.

3. Pixel Footprint Projection: Each pixel in the input images is projected onto the output image grid. This projection typically involves shrinking the input pixel by a "drizzle fraction" (usually between 0 and 1) and placing the shrunk pixel at the correct location in the output image, taking into account image alignment and distortion corrections.

4. Weighting and Accumulation: As pixels from different input images fall onto the same region of the output image, their flux values are weighted (typically based on noise characteristics) and accumulated. Areas with more overlap will have a higher total weight.

5. Normalization: The final output image is normalized by dividing the accumulated flux by the accumulated weight. This ensures that the flux values are correctly scaled.

Drizzling offers several advantages:

• Improved Resolution: By mapping input pixels onto a finer grid, drizzling can effectively increase the spatial resolution of the image, recovering details that would otherwise be lost.

• Reduced Aliasing: Undersampling can lead to aliasing artifacts in images. Drizzling helps to reduce aliasing by spreading the information from each pixel over a larger area.

• Enhanced Signal-to-Noise Ratio: By combining multiple images, drizzling can improve the signal-to-noise ratio of the final image.

• Artifact Removal: Drizzling can help to mitigate the effects of cosmic rays and other artifacts present in the input images.

Drizzling has become a standard technique in astronomical image processing and is widely used for analyzing data from telescopes such as HST and JWST. While originally developed for astronomical data, the underlying principles of drizzling can be applied to other image processing applications where undersampling or combining multiple images is necessary.