6533b821fe1ef96bd127c1de

RESEARCH PRODUCT

LDR Image to HDR Image Mapping with Overexposure Preprocessing

Fan YangBo GuYongqing HuoVincent Brost

subject

[INFO.INFO-AR]Computer Science [cs]/Hardware Architecture [cs.AR]Image qualityComputer scienceImage mapPrincipal component analysisComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONHDR02 engineering and technologyImage (mathematics)Highlight removal0202 electrical engineering electronic engineering information engineeringPreprocessorComputer visionElectrical and Electronic EngineeringComputingMilieux_MISCELLANEOUSHigh dynamic rangeExposurebusiness.industryDynamic rangeApplied MathematicsImage quality metric020207 software engineeringComputer Graphics and Computer-Aided DesignOverexposed areaSignal ProcessingMetric (mathematics)020201 artificial intelligence & image processing[ INFO.INFO-AR ] Computer Science [cs]/Hardware Architecture [cs.AR]Artificial intelligencebusiness

description

International audience; Due to the growing popularity of High Dynamic Range (HDR) images and HDR displays, a large amount of existing Low Dynamic Range (LDR) images are required to be converted to HDR format to benefit HDR advantages, which give rise to some LDR to HDR algorithms. Most of these algorithms especially tackle overexposed areas during expanding, which is the potential to make the image quality worse than that before processing and introduces artifacts. To dispel these problems, we . present a new,LDR to HDR approach, unlike the existing techniques, it focuses on avoiding sophisticated treatment to overexposed areas in dynamic range expansion step. Based on a separating principle, firstly, according to the familiar types of overexposure, the overexposed areas are classified into two categories which are removed and corrected respectively by two kinds of techniques. Secondly, for maintaining color consistency, color recovery is carried out to the preprocessed images. Finally, the LDR image is expanded to HDR. Experiments show that the proposed approach performs well and produced images become more favorable and suitable for applications. The image quality metric also illustrates that we can reveal more details without causing artifacts introduced by other algorithms.

https://doi.org/10.1587/transfun.e96.a.1185