Open AccessConstruction of high dynamic range image based on gradient information transformation
Author(s) -
Liu Yanyu,
Zhou Dongming,
Nie Rencan,
Hou Ruichao,
Ding Zhaisheng
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0118
Subject(s) - luminance , artificial intelligence , computer vision , pixel , computer science , contourlet , leverage (statistics) , transformation (genetics) , high dynamic range , distortion (music) , pattern recognition (psychology) , contrast (vision) , image fusion , dynamic range , mathematics , image (mathematics) , wavelet transform , wavelet , bandwidth (computing) , amplifier , biochemistry , chemistry , computer network , gene
This study proposes a fusion method for high dynamic range images based on gradient information transformation. In the proposed work, the authors first measure the three exposure weights of the source images, namely, local contrast, luminance and spatial structure. Then, the exposure weights are merged through a multi‐scale Laplacian pyramid scheme. For the weight maps measurement, the dense scale‐invariant feature transform method is used to calculate the local contrast around each pixel location, rather than a single pixel. The image luminance levels are computed in the gradient domain to get more visual information and the authors leverage the dictionary learning to effectively extract the luminance of images. Additionally, to better preserve the spatial structure of the source images, the just‐noticeable‐distortion technique is employed. By comparing the experimental results both subjectively and objectively, it is evident that the proposed method represents an improvement over some exciting methods.