AN EFFECTIVE IMAGE DEBLURRING SCHEME USING CLUSTER BASED SPARSE REPRESENTATION
DOI:
https://doi.org/10.11113/aej.v11.17861Keywords:
Cluster of patches, Image restoration, Mixed blur, Self-similarity, Sparse codingAbstract
Sparse based representation is being used extensively for image restoration. The dictionary learning
through patch extraction is central to the sparse based schemes. In the process of dictionary learning,
a large number of patches will be extracted from high quality images and dictionary will be formed.
Hence, over-complete dictionaries will be built. To overcome the complexity associated with overcomplete
dictionaries many schemes were proposed. Of them, the adaptive sparse domain is the
popular one. Many variations of adaptive sparse domain schemes were proposed. Selection of obvious
patches is common to all. In all these schemes, individual patches will be considered as the basic entity
and will be used. This is the reason for the complexity involved in sparse representation. In this paper,
to avoid the complexity, the patches are grouped according to the similarity among the patches. In
addition to reduce the complexity the proposed cluster based scheme considers the self-similarity of
the patches involved. Hence better performance with less complexity is possible with the proposed
schemes. In the process of testing, in addition to uniform blur and Gaussian blur, a combination of the
two blurs is also considered.
