High Dynamic Range Image Display With Halo and Clipping Prevention

Abstract

The dynamic range of an image is defined as the ratio between the highest and the lowest luminance level. In a high dynamic range (HDR) image, this value exceeds the capabilities of conventional display devices; as a consequence, dedicated visualization techniques are required. In particular, it is possible to process an HDR image in order to reduce its dynamic range without producing a significant change in the visual sensation experienced by the observer. In this paper, we propose a dynamic range reduction algorithm that produces high-quality results with a low computational cost and a limited number of parameters. The algorithm belongs to the category of methods based upon the Retinex theory of vision and was specifically designed in order to prevent the formation of common artifacts, such as halos around the sharp edges and clipping of the highlights, that often affect methods of this kind. After a detailed analysis of the state of the art, we shall describe the method and compare the results and performance with those of two techniques recently proposed in the literature and one commercial software.

Comparative Analysis between LDR and HDR Images for Automatic Fruit Recognition and Counting

“…However, most devices can only reproduce, at most, 2-3 orders of magnitude [24]. In a regular camera, the brightness levels are only 256 (8 bits), unsuitable for actual scenes, resulting in images with very dark or very bright areas [23,25].…”

Edge-guided filtering scheme for decomposition-based tone mapping

“…Lee et al [12] segmented the input HDR using K-means algorithm and set a display gamma parameter automatically for each segmented region. Guarnieri et al [13] specifically designed an algorithm based upon Retinex theory of vision in order to prevent the formation of common artifacts.…”

Multiexposure image fusion using intensity enhancement and detail extraction

“…Multiexposure image fusion directly combines the information within LDR images with different exposures into an HDR-like image and aims at dynamic range enhancement and detail preserving [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33]. As compared with typical HDR imaging containing HDR reconstruction and tone mapping, multiexposure image fusion is computationally efficient.…”

“…Guarnieri et al [33] used a dynamic range reduction algorithm to produce high-quality HDR-like images. This algorithm, based on the Retinex theory of vision, is specifically designed to prevent the formation of common artifacts, such as halos and highlight clippings.…”