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High dynamic range imaging by perceptual logarithmic exposure merging

Florea C., Vertan C., Florea L.

International Journal of Applied Mathematics and Computer Science

2015

Vol. 25, no. 4

943--954

In this paper we emphasize a similarity between the logarithmic type image processing (LTIP) model and the Naka–Rushton model of the human visual system (HVS). LTIP is a derivation of logarithmic image processing (LIP), which further replaces the logarithmic function with a ratio of polynomial functions. Based on this similarity, we show that it is possible to present a unifying framework for the high dynamic range (HDR) imaging problem, namely, that performing exposure merging under the LTIP model is equivalent to standard irradiance map fusion. The resulting HDR algorithm is shown to provide high quality in both subjective and objective evaluations.

Wspomaganie sprzętowe do wyznaczenia statystyk obrazów naturalnych wyższego rzędu

Tomaszewska A.

Pomiary Automatyka Kontrola

2011

R. 57, nr 8

899-901

Statystyki obrazów naturalnych, definiowanych jako nieprzetworzone obrazy rejestrowane przez człowieka, charakteryzują się dużą regularnością. Ich cechy wykorzystywane są w wielu aplikacjach grafiki komputerowej takich jak usuwanie szumu, czy kompresja. W artykule przedstawiono algorytm do szybkiego obliczenia statystyk wyższego rzędu na podstawie współczynników falek z wykorzystaniem programowalnego procsora graficznego. W rezultatach przedstawiono wyniki przyspieszenia uzyskanego przy wykorzystaniu GPU w porównaniu z implementacją na CPU.

A natural image is unprocessed reproduction of a natural scene observed by a human. The Human Visual System (HVS), during its evolution, has been adjusted to the information encoded in natural images. Computer images are interpreted best by a human when they fit natural image statistics that can model the information in natural images. The main requirement of such statistics is their striking regularity. It hepls separate the information from noise, reconstruct information which is not avaiable in an image, or only partially avaiable. Other applications of statistics is compression, texture synthesis or finding distortion model in image like blur kernel. The statistics are translation and scale invariant, therefore a distribution of statistics does not depend on the object position in the image and on its size. In this paper there are presented higher order natural image statistics calculations based on GPU. The characteristic of the statistics is that they are independent of the scale and rotation transformations. Therefore, they are suitable for many graphic applications. To analyze images there is used statistics computed in the wavelet domain and there is considered the image contrast. The computation speedup is presented in the results. The paper is organized as follows: the overview of natural images sta-tistics is introduced in Section 2. In Section 3 the GPU-based implementation is described. The obtained results are given in Section 4. Finally, there are presented the concluding remarks.