  Enhancing low resolution images via super-resolution or image synthesis for
cross-resolution face recognition has been well studied. Several image
processing and machine learning paradigms have been explored for addressing the
same. In this research, we propose Synthesis via Deep Sparse Representation
algorithm for synthesizing a high resolution face image from a low resolution
input image. The proposed algorithm learns multi-level sparse representation
for both high and low resolution gallery images, along with an identity aware
dictionary and a transformation function between the two representations for
face identification scenarios. With low resolution test data as input, the high
resolution test image is synthesized using the identity aware dictionary and
transformation which is then used for face recognition. The performance of the
proposed SDSR algorithm is evaluated on four databases, including one real
world dataset. Experimental results and comparison with existing seven
algorithms demonstrate the efficacy of the proposed algorithm in terms of both
face identification and image quality measures.
