Image Processing for Human Understanding in Low-visibility

Low-visibility conditions for navigation of vehicles are a frequent occurrence. Driving at night, in blizzards, in sand storms, or in fog form an obvious set of challenging conditions. Remote operation of unmanned vehicles through a camera image provides a similar difficulty. Gathering intelligence from satellite imagery can similarly benefit from improved visibility. Advanced image processing techniques (e.g., contrast enhancement or tone mapping) purport to improve the perceptual quality of images that lack the contrast or color depth perceived by the human visual system (HVS). Applying such an algorithm intelligently to these low-visibility conditions gives us the ability to provide a perceptually usable assisted-vision system. One premiere method for perceptual enhancement emerged from Retinex theory (Land, 1977; McCann, 2004). The key observation was that perceived color and intensity of a region in an image depend on not only inherent color and intensity, but also color and intensity of surrounding regions and on lighting. This property of the HVS serves as the basis for Retinex theory and numerous applications of it that process digital images to adjust color and intensity for the perceptual advantage of a human observer. Original Retinex-based algorithms assumed that an image was underexposed; extensions have enabled contrast enhancement in overexposed images to be darkened for human perception. We contribute flexibility to Retinex processing by automatically determining multiple intensity levels from which brightening and darkening of imagery may be performed. We identify spatial areas in each color channel with similar intensity and therefore low local contrast. Pixels close to these intensity levels are treated with extra emphasis to reveal detail that may have previously been hidden to the eye. Each identified level is weighted against the others based on the prevalence. We show that our adaptation improves local contrast in a varied set of test cases which would benefit from perceptual enhancement. Figure 1: A low-visibility image and our enhanced version