Method to produce a full-color smoothed image

The present invention aims at providing a method to produce modified images of a human face while removing all defects, such as such as pigmentation irregularities (e.g., moles, freckles), irritation, roughness, or wrinkles This is achieved thanks to a method to produce a full-color smoothed image comprising the steps of : - acquiring a first image in the visible spectrum of a scene, - acquiring a second image in the near infra-red spectrum of the same or similar scene, - extracting the intensity information from the first image, - decomposing the intensity information from the first image into a first detail layer and a first base layer, - decomposing the second image into a second detail layer and a second base layer, - combining at least all or part of the second detail layer with at least all or part of the first base layer to produce a combined intensity information, - combining the color information of the first image with the combined intensity information to produce the full-color smoothed image

Exploiting redundancy in color images

Joanna Marguier

The colors in a digital image do not only depend on the scene's reflectances, but also on the lighting conditions and on the characteristics of the imaging device. It is often necessary to separate the influence of these three elements, in particular to separate the contribution of the object's reflection properties from the one of the incident light. Both are usually represented by spectral quantities, i.e., defined over the visible spectrum. The retrieval of illuminant and reflectances from an RGB image, which provides three values per pixel, is an under-determined problem and requires additional information or assumption on the scene's content in order to be solved. In this thesis, we study how redundant information across a set of images can be exploited to recover either illuminant or reflectance knowledge. We demonstrate that accurate object color can be retrieved as tristimulus values from standard RGB images captured with an unknown camera under uncontrolled lighting conditions and no access to raw data, but containing a limited number of reference colors in the form of a specific calibration target. The correction must be constrained to a limited range of colors similar to the one of the object of interest, which are compared to pre-computed references for the derivation of a linear transform. We implement this method for two consumer oriented applications in Makeup and Home Décor, respectively, which provide users with color advice. The correction accuracy is of ΔE*ab ≃ 1 to 2 depending on the design of the target. We show that this accuracy is sufficient for the applications at hand. We also developed a method to retrieve illuminant spectra from a set of images taken with fixed location cameras, such as panoramic or surveillance ones. In pictures captured with such devices, there will be changes in lighting and dynamic content, but there will also be constant objects. We propose to use these elements as reference colors to solve for color constancy. More precisely, we exploit that their reflectances, while unknown, remain constant across images. We invert a series of image formation models in parallel for a set of test illuminants and, by forcing the output reflectances to match, deduce the illuminant under which each image was captured. Simulations on synthetic RGB patches demonstrate that real daylight illuminants can be retrieved with a median angular under 3° from sets containing as little as four images using a limited number of reference surfaces.

EPFL2010

GUI-Aided NIR and Color Image Blending

Radhakrishna Achanta, Andrea Guidi, Sabine Süsstrunk

A near-infrared (NIR) image of a scene contains different information than the corresponding visible spectrum (RGB) image, due to physical phenomena related to scattering, absorption, and reflectance of the different radiation bands. It can be desirable to combine the most relevant image content from both NIR and RGB to improve image quality. In this paper, we present two schemes of combining NIR and RGB information to obtain visually better quality images. The first is an automatic approach that performs segment-based NIR-blending by local and global entropy maximization. The second uses a GUI-aided approach where the user chooses the segments and manually con- trols blending. To minimize artifacts at the segment boundaries, three different boundary smoothing methods are introduced and compared in terms of speed and quality of output.

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Illuminant estimation and detection using near infrared

Sabine Süsstrunk

Digital camera sensors are sensitive to wavelengths ranging from the ultraviolet (200-400nm) to the near-infrared (700-100nm) bands. This range is, however, reduced because the aim of photographic cameras is to capture and reproduce the visible spectrum (400-700nm) only. Ultraviolet radiation is filtered out by the optical elements of the camera, while a specifically designed "hot-mirror" is placed in front of the sensor to prevent near-infrared contamination of the visible image. We propose that near-infrared data can actually prove remarkably useful in colour constancy, to estimate the incident illumination as well as providing to detect the location of different illuminants in a multiply lit scene. Looking at common illuminants spectral power distribution show that very strong differences exist between the near-infrared and visible bands, e.g., incandescent illumination peaks in the near-infrared while fluorescent sources are mostly confined to the visible band. We show that illuminants can be estimated by simply looking at the ratios of two images: a standard RGB image and a near-infrared only image. As the differences between illuminants are amplified in the near-infrared, this estimation proves to be more reliable than using only the visible band. Furthermore, in most multiple illumination situations, one of the light will be predominantly near-infrared emitting (e.g., flash, incandescent) while the other will be mostly visible emitting (e.g., fluorescent, skylight). Using near-infrared and RGB image ratios allow us to accurately pinpoint the location of diverse illuminant and recover a lighting map.

2009

Exploiting redundancy in color images

Joanna Marguier

EPFL2010