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

Abstract

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.

Consistent joint photometric and geometric image registration

“…In a controlled experiment, we perform a quantitative evaluation of the joint geometric and photometric registration algorithms according to the affine/linear model (9). We simulate 10 degraded 512 × 512 image pairs (the so-called reference and target images) from 10 random test images by successively applying (i) a Tukey windowing function (to prevent the influence of non-overlapping regions due to the spatial deformations [17] We compare the proposed TLS solution as described in Section 4 to its OLS counterpart (via the Gauss-Newton optimization, a similar derivation for geometric registration is given in [15]) and the joint geometric linear and photometric affine registration algorithm of Bartoli [5], which operates in an inverse compositional gradientbased framework using the ordinary least square metrics.…”

“…They computed the comparametric parameters directly from cumulative intensity histograms. Candocia approximated the comparametric function and the camera response function by a piecewise linear model [8,9]. Gevrekci and Gunturk employed a geometric feature point matching algorithm and comparametric regression to perform joint HDR and super-resolution reconstruction [10].…”

“…f (x, y) = a1g(a00 + a10x + a01y, b00 + b10x + b01y) + a0, (9) Like the gradient-based geometric registration algorithms in [15], we iteratively estimate the registration parameters using the incremental updates δpG = [ δa00 δa10 δa01 δb00 δb10 δb01 ] T and…”

Piecewise-consistent color mappings of images acquired under various conditions

“…Methods for computing color mapping are based on either pixel-to-pixel correspondence, e.g., [2], or based on the statistical distributions of color values [12,13,14]. In the statistical approaches, exact correspondence is not required although correctness is not assured (see Sec.…”

An Evaluation Framework for the Accuracy of Camera Transfer Functions Estimated from Differently Exposed Images

“…temperature). An alternative to chart-based measurements of the CTF is to use differently exposed images of the same scene [10,8,9,4,11,5,6,1,2]. Assuming that the exposure ratios of image pairs are known and the CTF can be modeled by, e.g., a γ-function…”