Image reconstruction algorithm from compressed sensing measurements by dictionary learning

• Published in: Neurocomputing (Amsterdam) Vol. 151; pp. 1153 - 1162
• Main Authors: Shen, Yanfei, Li, Jintao, Zhu, Zhenmin, Cao, Wei, Song, Yun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 03.03.2015
• Subjects: Algorithms; Coding; Compressed; Compressed sensing; Detection; Dictionaries; Dictionary learning; Image enhancement; Image quality; Image reconstruction; Linearization technique; OMP; Linearization technique; OMP; Compressed sensing; Image reconstruction; Dictionary learning
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2014.06.082

It is a challenge task to reconstruct images from compressed sensing measurement due to its implicit ill-posed property. In this paper, we propose an image reconstruction algorithm for compressed sensing image application based on the adaptive dictionary, which is learned from the reconstructed image itself. The sparsity level is enhanced since the sparse coding of overlapping image patches emphasizes the local image features; accordingly the quality of the reconstructed image is also improved. In addition, Batch-OMP algorithm, linearization technique and dynamic updating sparse coding algorithm are used to reduce the computational complexity of our proposed algorithm. Numerical experiments are conducted on several test images with a variety of sampling ratios. The results demonstrate that our proposed algorithm can efficiently reconstruct images from compressed sensing measurements and achieve more than 3dB gain averagely over the current leading CS reconstruction algorithm.