We present a method to automatically detect shadows in a fast and accurate manner by taking advantage of the inherent sensitivity of digital camera sensors to the near-infrared (NIR) part of the spectrum. Dark objects, which confound many shadow detection algorithms, often have much higher reflectance in the NIR. We can thus build an accurate shadow candidate map based on image pixels that are dark both in the visible and NIR representations. We further refine the shadow map by incorporating ratios of the visible to the NIR image, based on the observation that commonly encountered light sources have very distinct spectra in the NIR band. The results are validated on a new database, which contains visible/NIR images for a large variety of real-world shadow creating illuminant conditions, as well as manually labeled shadow ground truth. Both quantitative and qualitative evaluations show that our method outperforms current state-of-the-art shadow detection algorithms in terms of accuracy and computational efficiency.

Skin tones, portraits in particular, are of critical importance in photography and video, but a number of factors, such as pigmentation irregularities (e.g., moles, freckles), irritation, roughness, or wrinkles can reduce their appeal. Moreover, such "defects" are oftentimes enhanced by scene lighting conditions.

Digital camera sensors are inherently sensitive to the near-infrared (NIR) part of the light spectrum. In this paper, we propose a general design for color filter arrays that allow the joint capture of visible/NIR images using a single sensor. We pose the CFA design as a novel spatial domain optimization problem, and provide an efficient iterative procedure that finds (locally) optimal solutions. Numerical experiments confirm the effectiveness of the proposed CFA design, which can simultaneously capture high quality visible and NIR image pairs.

In landscape photography, distant objects often appear blurred with a blue color cast, a degradation caused by atmospheric haze. To enhance image contrast, pleasantness and information content, dehazing can be performed.

In this paper we present a surprisingly simple yet powerful method for detecting illumination—determining which pixels are lit by different lights—in images. Our method is based on the chromagenic camera, which takes two pictures of each scene: one is captured as normal and the other through a coloured filter. Previous research has shown that the relationship between the colours, the RGBs, in the filtered and unfiltered images depends strongly on the colour of the light and this can be used to estimate the colour of the illuminant. While chromagenic illuminant estimation often works well it can and does fail and so is not itself a direct solution to the illuminant detection problem. In this paper we dispense with the goal of illumination estimation and seek only to use the chromagenic effect to find out which parts of a scene are illuminated by the same lights. The simplest implementation of our idea involves a combinatorial search. We precompute a dictionary of possible illuminant relations—that might map RGBs to filtered counterparts—from which we select a small number 𝑚 corresponding to the number of distinct lights we think might be present. Each pixel, or region, is assigned the relation from this 𝑚-set that best maps filtered to unfiltered RGB. All 𝑚-sets are tried in turn and the one that has the minimum prediction error over all is found. At the end of this search process each pixel or region is assigned an integer between 1 and 𝑚 indicating which of the m lights are thought to have illuminated the region. Our simple search algorithm is possible when 𝑚 = 2 (and 𝑚 = 3) and for this case we present experiments that show our method does a remarkable job in detecting illumination in images: if the 2 lights are shadow and non- shadow, we find the shadows almost effortlessly. Compared to ground truth data, our method delivers close to optimal performance.

Every time an object lies in the way of an illumination source, a shadow is cast. The shadow is only illuminated by the sky, difference

For certain databases and classification tasks, analyzing images based region features instead of image features results in more accurate classifications. We introduce eigenregions, which are geometrical features that encompass area, location, and shape properties of an image region, even if the region is spatially incoherent. Eigenregions are calculated using principal component analysis (PCA). On a database of 77,000 different regions obtained through the segmentation of 13,500 real-scene photographic images taken by nonprofessionals, eigenregions improved the detection of localized image classes by a noticeable amount. Additionally, eigenregions allow us to prove that the largest variance in natural image region geometry is due to its area and not to shape or position.