Correction of Dropped Frames in High-resolution Push-broom Hyperspectral Images for Cultural Heritage

Dropped frames can occur in line-scan cameras, which result in non-uniform spatial sampling of the scene. A dropped frame occurs when data from an image sensor is not successfully recorded. When mosaicking multiple line-scan images, such as in high-resolution imaging, this can cause misalignment. Much previous work to identify dropped frames in video prioritises fast computation over high accuracy, whereas in heritage imaging, high accuracy is often preferred over short computation time. Two approaches to identify the position of dropped frames are presented, both using the A* search algorithm to correct dropped frames. One method aligns overlapping sections of push-broom images and the other aligns the push-broom image to a lower resolution reference image. The two methods are compared across a range of test images, and the method aligning overlapping sections is shown to perform better than the method using a reference image under most circumstances. The overlap method was applied to hyperspectral images acquired of La Ghirlandata, an 1873 oil on canvas painting by D. G. Rossetti, enabling a high-resolution hyperspectral image mosaic to be produced. The resulting composite image is 10,875 x 14,697 pixels each with 500 spectral bands from 400 2,500 nm. This corresponds to a spatial resolution of 80 pm and a spectral resolution of 3-6 nm.

Automated summary

This paper introduces two approaches to correct dropped frames in line-scan cameras using the A* search algorithm. By comparing the two methods, it is found that aligning overlapping sections of images performs better than aligning to a reference image. These methods are then applied to mosaic high-resolution hyperspectral images of a cultural heritage painting, resulting in a composite image with high spatial and spectral resolution.