《Predicting the Quality of Fused Long Wave Infrared
and Visible Light Images》
1.we analyze five multi-resolution-based image fusion methods in regards to several common distortions, including blur, white
noise, JPEG compression, and non-uniformity.
2.The performance of different image fusion algorithms has been evaluated by image fusion quality metrics that are based on information theory [10]–[12], space and frequency based image features [13]–[16], image structural similarity [17]–[19], and models of human perception [20], [21].
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1.NU manifests as an undesirable grid-like pattern on images
obtained using focal plane arrays
2.while the “halo effect”
appears around very hot or cold objects in imagery from
uncalibrated ferroelectric BST sensors [24], causing regions
surrounding bright objects to grow darker, and regions around
dark objects to grow lighter
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Background-Subtraction in Thermal Imagery Using Contour Saliency
- human silhouette extraction is unreliable
when some part of the human body or clothing has the
temperature similar to the background temperature. In addition,
human body casts obvious projection on smooth surfaces such as a smooth floor.《Fusion of color and infrared video for moving human detection》
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LWIR images,
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