This paper presents a method for reducing undesirable tonal fluctuations in video: minute changes in tonal characteristics, such as exposure, color temperature, brightness and contrast in a sequence of frames, which are easily noticeable when the sequence is viewed. These fluctuations are typically caused by the camera's automatic adjustment of its tonal settings while shooting.Our approach operates on a continuous video shot by first designating one or more frames as anchors. We then tonally align a sequence of frames with each anchor: for each frame, we compute an adjustment map that indicates how each of its pixels should be modified in order to appear as if it was captured with the tonal settings of the anchor. The adjustment map is efficiently updated between successive frames by taking advantage of temporal video coherence and the global nature of the tonal fluctuations. Once a sequence has been aligned, it is possible to generate smooth tonal transitions between anchors, and also further control its tonal characteristics in a consistent and principled manner, which is difficult to do without incurring strong artifacts when operating on unstable sequences. We demonstrate the utility of our method using a number of clips captured with a variety of video cameras, and believe that it is well-suited for integration into today's non-linear video editing tools.

Tonal stabilization of video

Many applications in computer vision require comparisons between two images of the same scene. Comparison applications usually assume that corresponding regions in the two images have similar colors. However, this assumption is not always true. One way to deal with this problem is to apply a color mapping to one of the images. In this paper we address the challenge of computing color mappings between pairs of images acquired under different acquisition conditions, and possibly by different cameras. For images taken from different viewpoints, our proposed method overcomes the lack of pixel correspondence. For images taken under different illumination, we show that no single color mapping exists, and we address and solve a new problem of computing a minimal set of piecewise color mappings. When both viewpoint and illumination vary, our method can only handle planar regions of the scene. In this case, the scene planar regions are simultaneously co-segmented in the two images, and piecewise color mappings for these regions are calculated. We demonstrate applications of the proposed method for each of these cases.

Piecewise-consistent color mappings of images acquired under various conditions

Capturing natural scenes with high dynamic range content using conventional RGB cameras generally results in saturated and underexposed and therefore compromising image areas. Furthermore the image lacks color accuracy due to a systematic color error of the RGB color filters. The problem of the limited dynamic range of the camera has been addressed by high dynamic range imaging1, 2 (HDRI): Several RGB images of different exposures are combined into one image with greater dynamic range. Color accuracy on the other hand can be greatly improved using multispectral cameras,3 which more accurately sample the electromagnetic spectrum. We present a promising combination of both technologies, a high dynamic range multispectral camera featuring a higher color accuracy, an improved signal to noise ratio and greater dynamic range compared to a similar low dynamic range camera.© (2008) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

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Multispectral high dynamic range imaging

Joint photometric and geometric image registration in the total least square sense

Cancer is one of the most common causes of death. Cytopathological, i.e., cell-based, diagnosis of cancer can be applied in screening scenarios and allows an early and highly sensitive detection of cancer, thus increasing the chance for cure. The detection of cancer on cells addressed in this paper is based on bright field light microscopy. The cells are imaged with a camera mounted on a microscope, allowing to measure cell properties. However, these cameras exhibit only a limited dynamic range, which often makes the quantification of properties difficult or even impossible. Consequently, to allow a computer-assisted analysis of microscopy images, the imaging has to be improved. To this end, we show how the dynamic range can be increased by acquiring a set of differently exposed cell images. These high dynamic range (HDR) images allow to measure cellular features that are otherwise difficult to capture, if at all. We show that HDR microscopy not only increases the dynamic range, but furthermore reduces noise and improves the acquisition of colors. We develop HDR microscopy-based algorithms, which are essential for cytopathological oncology and early cancer detection and only possible with HDR microscopy imaging. We show the detection of certain subcellular features, so-called AgNORs, in silver (Ag) stained specimens. Furthermore, we give examples of two further applications, namely: 1) the detection of stained cells in immunocytochemical preparations and 2) color separation for nuclear segmentation of specimens stained with low contrast.

High Dynamic Range Microscopy for Cytopathological Cancer Diagnosis

A fundamentally new approach that accurately estimates the camera response function from comparametric data, i.e., pixel data from two differently exposed images over a common field of view, is presented. It does so by solving for the camera response function from its associated comparametric relation. The approach offers several advantageous features, including having a complexity that is independent of the number of pixel data considered, allowing for the modeling of saturated pixels, enabling an inherently constrained optimization problem to be solved in an unconstrained manner, and the easy incorporation into an existing framework for joint image registration. This is accomplished by approximating the camera response function with a constrained piecewise linear model so that its solution, within the comparametric camera relation, can be obtained. This results in a semiparametric comparametric model, optimally determined from pixel data, which is directly parameterized in terms of the exposure parameter. Subsequently, it is shown how this semiparametric model is used for exposure estimation from captured images. Finally, we incorporate the semiparametric model within an existing and previously published framework for simultaneous and joint spatial and tonal image registration in order to illustrate the developed model's performance.

D.A. Mandarino

Papers

A semiparametric model for accurate camera response function modeling and exposure estimation from comparametric data