High dynamic range image rendering with a retinex-based adaptive filter

Abstract

We propose a new method to render high dynamic range images that models global and local adaptation of the human visual system. Our method is based on the center-surround Retinex model. The novelties of our method is first to use an adaptive filter, whose shape follows the image high-contrast edges, thus reducing halo artifacts common to other methods. Second, only the luminance channel is processed, which is defined by the first component of a principal component analysis. Principal component analysis provides orthogonality between channels and thus reduces the chromatic changes caused by the modification of luminance. We show that our method efficiently renders high dynamic range images and we compare our results with the current state of the art.

QBRIX: a quantile-based approach to retinex

“…An overview is out of the scope of this paper; however, these implementations can be divided into two major groups and differ in the ways they achieve locality. The first group, among which we can mention RSR, [1,9,11,[19][20][21] uses a sampling approach: the neighborhood of each pixel is explored either using paths or extracting random pixels; the second group [23,[29][30][31][32] computes values over the image with convolution masks or weighting distances. An extensive review on retinex, including recent PDE and variational implementations, can be found in [33].…”

“…Among typical purposes and applications are color constancy [3][4][5], separation of illumination from reflectance [6], shadow removal [7], HDR imaging [8,9], human vision modeling [1,10,11], photographic dynamic range rendering [12,13], color adjustment for pictures taken under unknown lighting conditions [14,15], and unsupervised color movie restoration [16].…”

Contrast Enhancement of Color Images Using Improved Retinex Method

“…This problem can be removed by using color restoration method. Thus a color restoration factor (CRF) block is added with the MSR block to obtain the MSRCR algorithm Main problems of MSRCR algorithm are the presence of halo artifacts at edges, graying out [16] of low contrast areas and bad color rendition. The MSRCR has halo artifacts [19] in high contrast edges.…”

“…The MSRCR has halo artifacts [19] in high contrast edges. The greying out effect of MSRCR is reduced by using adaptive filtering [16] on luminance channel. At high contrast edges, these adaptive filter adapt the shape of the filter.…”

“…At high contrast edges, these adaptive filter adapt the shape of the filter. In this way they reduces the greying out and halo artifacts [16].But even then halo artifacts remains in images enhanced by using adaptive image enhancement methods [19]. Halo artifacts in color images using, a fast edge preserving filter [20].But it reduces the contrast.…”