Abstract: The style of HDR image includes natural, reliefs, drawings, paintings and so on. It shows the
rich artistic qualities, has the characteristics of remarkable style, rich in detail, full color and others.
However, the conventional method for generating HDR style is tedious, time consuming and the effect
of HDR is instability. Therefore, in order to simplify the interaction and enhance the effect of the result,
we proposed a style transfer method for HDR image by giving an HDR reference image and using color
transfer and dictionary learning technology to transfer the features of HDR photograph to the source
image so that to generate the HDR image automatically. First, with the help of gradient-preserving color
transfer technology, the color feature of the HDR reference image was transferred to the source photo.
Then, extracting the detail features of the HDR reference image and using K_SVD algorithm to train a
dictionary to form an over-complete dictionary set of the details. Next, extracting the detail features of
source image and using the dictionary set to sparse reconstruction and then generating the right HDR
image with detail features. Finally, combining the result of color transfer with the sparse reconstruction
result of the source photo to generate a new style of HDR image. The experiment results show that the
method applied to a variety of HDR styles can obtain the same visual effects as the reference image.

Key words: HDR style, color transfer, dictionary learning, K-SVD, sparse reconstruction