TL;DR: It is argued that exposure adjustment by gamma correction is inherently flawed, and alternatives are provided, which give rise to a new kind of processing in the "amplitude domain".
Abstract: It is argued that, hidden within the flow of signals from typical cameras, through image processing, to display media, is a homomorphic filter. While homomorphic filtering is often desirable, there are some occasions where it is not. Thus, cancellation of this implicit homomorphic filter is proposed, through the introduction of an antihomomorphic filter. This concept gives rise to the principle of quantigraphic image processing, wherein it is argued that most cameras can be modeled as an array of idealized light meters each linearly responsive to a semi-monotonic function of the quantity of light received, integrated over a fixed spectral response profile. This quantity depends only on the spectral response of the sensor elements in the camera. A particular class of functional equations, called comparametric equations, is introduced as a basis for quantigraphic image processing. These are fundamental to the analysis and processing of multiple images differing only in exposure. The "gamma correction" of an image is presented as a simple example of a comparametric equation, for which it is shown that the underlying quantigraphic function does not pass through the origin. Thus, it is argued that exposure adjustment by gamma correction is inherently flawed, and alternatives are provided. These alternatives, when applied to a plurality of images that differ only in exposure, give rise to a new kind of processing in the "amplitude domain". The theoretical framework presented in this paper is applicable to the processing of images from nearly all types of modern cameras. This paper is a much revised draft of a 1992 peer-reviewed but unpublished report by the author, entitled "Lightspace and the Wyckoff principle".
TL;DR: An approach is presented that can simultaneously align multiple exposure-adjusted pictures of the same scene both in their spatial coordinates as well as in their pixel values to address the misalignment problem common to methods that compose mosaics from only pair-wise registered image pairs.
Abstract: An approach is presented that can simultaneously align multiple exposure-adjusted pictures of the same scene both in their spatial coordinates as well as in their pixel values. The approach is featureless and produces an image mosaic at a common spatial and exposure reference and also addresses the misalignment problem common to methods that compose mosaics from only pair-wise registered image pairs. The objective function considered minimizes the sum of the collective variance over pixels of a global coordinate grid on which to create the final image. The models employed relate images spatially by homographic transformations and tonally by comparametric functions. The importance of performing joint spatial and tonal registration on exposure-adjusted images is emphasized by providing two examples in which spatial-only registration fails. The performance between pair-wise and simultaneous registration under both spatial-only and joint registration procedures is discussed.
TL;DR: 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 and incorporated into an existing framework for joint image registration.
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.