  Image enhancement is an important image processing technique that processes
images suitably for a specific application e.g. image editing. The conventional
solutions of image enhancement are grouped into two categories which are
spatial domain processing method and transform domain processing method such as
contrast manipulation, histogram equalization, homomorphic filtering. This
paper proposes a new image enhance method based on dictionary learning.
Particularly, the proposed method adjusts the image by manipulating the rarity
of dictionary atoms. Firstly, learn the dictionary through sparse coding
algorithms on divided sub-image blocks. Secondly, compute the rarity of
dictionary atoms on statistics of the corresponding sparse coefficients.
Thirdly, adjust the rarity according to specific application and form a new
dictionary. Finally, reconstruct the image using the updated dictionary and
sparse coefficients. Compared with the traditional techniques, the proposed
method enhances image based on the image content not on distribution of pixel
grey value or frequency. The advantages of the proposed method lie in that it
is in better correspondence with the response of the human visual system and
more suitable for salient objects extraction. The experimental results
demonstrate the effectiveness of the proposed image enhance method.
