TY - GEN
T1 - Bio-inspired pixel-wise adaptive imaging
AU - Brinkworth, Russell S.A.
AU - Mäh, Eng Leng
AU - O'Carroll, David C.
PY - 2007/1/12
Y1 - 2007/1/12
N2 - The range of luminance levels in the natural world varies in the order of 108, significantly larger than the 8-bits employed by most digital imaging systems. To overcome their limited dynamic range traditional systems rely on the fact that the dynamic range of a scene is typically much lower, and by adjusting a global gain factor (shutter speed) it is possible to acquire usable images. However in many situations 8-bits of dynamic range is insufficient, meaning potentially useful information, lying outside of the dynamic range of the device, is lost. Traditional approaches to solving this have involved using nonlinear gamma tables to compress the range, hence reducing contrast in the digitized scene, or using 16-bit imaging devices, which use more bandwidth and are incompatible with most recording media and software post-processing techniques. This paper describes an algorithm, based on biological vision, which overcomes many of these problems. The algorithm reduces the redundancy of visual information and compresses the data observed in the real world into a significantly lower bandwidth signal, better suited for traditional 8-bit image processing and display. However, most importantly, no potentially useful information is lost and the contrast of the scene is enhanced in areas of high informational content (where there are changes) and reduced in areas containing low information content (where there are no changes). Thus making higher-order tasks, such as object identification and tracking, easier as redundant information has already been removed.
AB - The range of luminance levels in the natural world varies in the order of 108, significantly larger than the 8-bits employed by most digital imaging systems. To overcome their limited dynamic range traditional systems rely on the fact that the dynamic range of a scene is typically much lower, and by adjusting a global gain factor (shutter speed) it is possible to acquire usable images. However in many situations 8-bits of dynamic range is insufficient, meaning potentially useful information, lying outside of the dynamic range of the device, is lost. Traditional approaches to solving this have involved using nonlinear gamma tables to compress the range, hence reducing contrast in the digitized scene, or using 16-bit imaging devices, which use more bandwidth and are incompatible with most recording media and software post-processing techniques. This paper describes an algorithm, based on biological vision, which overcomes many of these problems. The algorithm reduces the redundancy of visual information and compresses the data observed in the real world into a significantly lower bandwidth signal, better suited for traditional 8-bit image processing and display. However, most importantly, no potentially useful information is lost and the contrast of the scene is enhanced in areas of high informational content (where there are changes) and reduced in areas containing low information content (where there are no changes). Thus making higher-order tasks, such as object identification and tracking, easier as redundant information has already been removed.
KW - Biological vision
KW - Biomimetic
KW - Photoreceptor
KW - Smart camera
KW - Video enhancement
KW - Visual processing
UR - http://www.scopus.com/inward/record.url?scp=34247399160&partnerID=8YFLogxK
U2 - 10.1117/12.695596
DO - 10.1117/12.695596
M3 - Conference contribution
AN - SCOPUS:34247399160
SN - 0819465232
SN - 9780819465238
VL - 6414
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Smart Structures, Devices, and Systems III
T2 - Smart Structures, Devices, and Systems III
Y2 - 11 December 2006 through 13 December 2006
ER -