Generating HDR radiance maps from single LDR image

Li Xiaofen; Huo Yongqing

doi:10.3969/j.issn.1003-501X.2017.06.002

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Li Xiaofen, Huo Yongqing. Generating HDR radiance maps from single LDR image[J]. Opto-Electronic Engineering, 2017, 44(6): 577-586. doi: 10.3969/j.issn.1003-501X.2017.06.002

Citation:

Li Xiaofen, Huo Yongqing. Generating HDR radiance maps from single LDR image[J]. Opto-Electronic Engineering, 2017, 44(6): 577-586. doi: 10.3969/j.issn.1003-501X.2017.06.002

Generating HDR radiance maps from single LDR image

Li Xiaofen,
Huo Yongqing^,

School of Communications and Information Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China

Fund Project: This work was supported by the National Natural Science Foundation of China (61401072).

More Information

Corresponding author: Huo Yongqing,E-mail: hyq980132@uestc.edu.cn

Received Date 12 March 2017

Revised Date 10 April 2017

Published Date 15 June 2017

Abstract

Abstract

We present a method of generating high dynamic range (HDR) radiance maps from a single low dynamic range (LDR) image and its camera response function (CRF). The method first models and estimates the inverse CRF and then multiplies the inverse CRF by a weighting function to make it smooth near the maximum and minimum pixel values, and finally conducts the smooth inverse CRF on the input LDR image to generate HDR image. In the method, the inverse CRF is estimated using one single LDR image and an empirical model of CRF, based on measured RGB distributions at color edges. Unlike most existing methods, the proposed method expands image from both high and low luminance regions. Thus, the algorithm can avoid the artifacts and detail loss in dark area which results from extending image only from bright region. Extensive experimental results show that the approach induces less contrast distortion and produces high visual quality HDR image.
- high dynamic range (HDR) image /
- low dynamic range (LDR) image /
- camera response function (CRF) /
- image quality metric

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References

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Overview

Overview

Abstract:We propose a method for generating high dynamic range (HDR) radiance maps from a single low dynamicrange (LDR) image and its camera response function (CRF). Most single-image based HDR image generation methods expand dynamic range only from bright areas which reduces details visible in shaded areas and induces artifacts atthe edge of bright areas. This inspires us to exploit an approach expanding dynamic range both from highlight regionand shaded region. The proposed method achieves this goal by performing inverse CRF on image intensity to recoverthe image irradiance which is taken as HDR image. The method first constructs inverse CRF model and computes itsoptimal solution, and then selects a weighting function and multiplies it by the optimal solution to make the inverseCRF smooth near the maximum and minimum pixel values, and finally conducts the smooth inverse CRF on the input LDR image to produce HDR image.
The proposed algorithm generates HDR image from single LDR image depending on inverse CRF reconstruction.The main steps include: inverse CRF estimation, inverse CRF smoothness, and HDR image generation. For inverseCRF estimation, the approach first models and then estimates inverse CRF based on the database established byGrossberg. The inverse CRF is reconstructed using the edge pixels in the LDR image based on the Grossberg’s DoRFdatabase and EMoR database, and the prior probability is empirically modeled as a Gaussian mixture model. Then, aBayesian framework is formed by combining the likelihood function with the prior model. Finally, the optimal inverseCRF is obtained by maximizing the posteriori probability (MAP). For inverse CRF smoothness, because the inverseCRF function typically has a steep slope near the minimal and maximal pixels, it is less smooth and non-monotonicnear these extremes. To solve this problem, we introduce a weighting function to make the function more smooth andreduce the effect of the pixels near the minimal and maximal in HDR image construction. The considerable choices ofweighting function are rectangular function, triangular function and Gaussian function. For HDR image generation,we can easily conduct the inverse CRF on the input LDR image to generate HDR image.
Unlike most existing methods, the proposed method expands image from both high and low luminance regions.Thus, the algorithm can avoid the artifacts and detail loss in dark area which results from extending image only frombright region. Extensive experimental results show that the approach induces less contrast distortion and produceshigh visual quality HDR image. The significance and novelty of the method include the smoothness function used inthe estimation of inverse CRF and the utilization of the inverse CRF in HDR image generation. These novelties realizeexpanding image both from bright and dark regions while guarantee the quality of generated HDR image.