High Dynamic Range Imaging Reconstruction by Asla M. Sa

By Asla M. Sa

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Extra resources for High Dynamic Range Imaging Reconstruction

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1 fˆ−1 (di j,k ) Then, compute radiance values the inverse of fˆ, as wi j = . 2 ANALYSIS AND CLASSIFICATION At this point, a question can be posed: What is the essential information necessary and sufficient to obtain the camera characteristic response function from images? The answer to this question will make precise a part of the problem statement that says: “select a sufficient number of pixel values”. This is important because it defines the conditions used on the data to solve the problem.

Grossberg and S. Nayar, “Determining the Camera Response From Images: What is Knowable? IEEE Trans. PAMI 25, 11 (November 2003), 1455–1467. [11] A. O. Aky¨uz and E. Reinhard, “Noise Reduction in High Dynamic Range Imaging,” Academic Press—Orlando, FL, USA, 18 (May 2007), pp. 366–376. [12] S. K. Nayar, “Modeling the Space of Camera Response Functions,” IEEE Trans. PAMI 26, 10 (October 2004), 1272–1282. [13] M. Goesele, New Acquisition Techniques for Real Objects and Light Sources in Computer Graphics.

Thus, the processing is performed using the luminance defined as L( p i j ) = Y (di j ), where Y is the video luminance channel. In this scheme, the tone-enhancement can be applied to any two consecutive fields. This produces an output video stream with the same input frame rate, as shown in Fig. 3. We assume that the frame rate is high compared to the object’s movement, thus, the effects of moving objects are small between a pair of fields. 3: Two consecutive input fields result in one frame. 4: Images (a) and (b) are the video input fields, while in (c) is shown the tonal-enhanced foreground.

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