"image difference" is a perfectly correct and usable phrase in written English.
You could use it to describe the contrast between two different pictures, such as "the image difference between the before-and-after photos of this remodeled kitchen is remarkable".
Image difference metrics are based on a number of different ideas but are usually following a general framework.
Many different databases have been used for evaluating the image difference metrics, but in order to extensively test WLF-DEE, we have chosen three databases: the public Tampere Image Database 2008 TID20088) and two databases developed at 'Norwegian Colour and Visual Computing Laboratory'.
However, at the moment, no universal image difference metric exists.
Methods included pixel-wise Normalized Difference Vegetation Index (NDVI) and Multiband Image Difference (MID) techniques.
Then, the proposal starts a dynamic image analysis phase based in optical flow or image difference.
Over the years, many image difference metrics have been proposed [2], some for measuring general image quality and image difference and some for detecting specific distortions.
Most applications of this type involve either image differencing or image classification using multi-temporal images.
Image differencing was then performed using the pre- and post-event band-ratio image pair.
The system is competitive with other tracking algorithms based on image differencing.
Models using combinations of post-fire Landsat bands perform much better than NBR, dNBR, image differencing, and image ratioing.
(ii) Image differencing change detection was employed to generate difference images with all the image-objects projected to a feature space consisting of both spectral and texture variables.
