Methods for Improving Image Quality and Reducing Data Load of NIR Hyperspectral Images

Abstract

Near Infrared Hyperspectral Imaging (NIRHSI) is an emerging technology platform that integrates conventional imaging and spectroscopy to attain both spatial and spectral information from an object. Two important problems in NIRHSI are those of data load and unserviceable pixels in the NIR sensor. Hyperspectral imaging experiments generate large amounts of data (typically > 50 MB per image), which tend to overwhelm the memory capacity of conventional computer systems. This inhibits the utilisation of NIRHSI for routine online industrial application. In general, approximately 1% of pixels in NIR detectors are unserviceable or ‘dead’, containing no useful information. While this percentage of pixels is insignificant for single wavelength imaging, the problem is amplified in NIRHSI, where > 100 wavelength images are typically acquired. This paper describes an approach for reducing the data load of hyperspectral experiments by using sample-specific vector-to-scalar operators for real time feature extraction and a systematic procedure for compensating for ‘dead’ pixels in the NIR sensor. The feasibility of this approach was tested for prediction of moisture content in carrot tissue.

Recent advancement of chemical imaging in pharmaceutical quality control: From final product testing to industrial utilization

“…It is estimated that approximately 1% of pixels are \dead pixels" in a NIR chemical image. 27 Dead pixels are anomalous pixels of zero or extreme high values with their sizes varying from a single one a cluster to a pixel line. A linear interpolation correction method can recover dead pixels, utilizing neighboring information of suspicion object.…”

“…A linear interpolation correction method can recover dead pixels, utilizing neighboring information of suspicion object. 27 Apart from that, chemical imagies could be in°uenced by illumination con¯guration or dark current of detectors or any other physical disturbance. Image calibration is therefore of great importance in terms of image quality improvement.…”

Hyperspectral monitoring of fructose content in marzipan

“…Last, the NIR bands around 1400-1500 nm are related to both O-H stretch vibrations and C-H combinations. (Westad, Afseth and Bro, 2007) Hyperspectral imaging has been also used on different food products; for example to measure the moisture content of carrots (Firtha, 2008 and2007), ham (Talens et al, 2003) and soy beans (Huang et al, 2013). The same technique was used on plants, too.…”

Chemometrics in Food Technology

“…But, the information derived from the analysis, contain also redundant information. This abundance of data has two drawback, one due to the high computational load of heavy data size and the second is due to the long acquisition times, given the size of the data being collected (Firtha et al 2008). Therefore, it is desirable to reduce the load to manageable levels, especially if the goal is the application of HSI techniques in real time, on-line on production lines.…”