Simultaneous image fusion and denoising with adaptive sparse representation

• Published in: IET image processing Vol. 9; no. 5; pp. 347 - 357
• Main Authors: Liu, Yu, Wang, Zengfu
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.05.2015
• Subjects: adaptive sparse representation; ASR model; compact sub‐dictionaries; Computer vision; gradient information; high computational cost; high‐quality image patches; image classification; image denoising; image fusion; Image processing; image reconstruction; image representation; learning (artificial intelligence); multifocus image sets; multimodal image sets; Noise reduction; objective assessment; potential visual artefacts; Redundant; Representations; signal modelling technique; signal reconstruction requirement; simultaneous image fusion; single redundant dictionary learning; source image patches; Strontium; Visual; visual quality; image processing; multifocus image sets; image classification; image fusion; gradient information; ASR model; single redundant dictionary learning; simultaneous image fusion; adaptive sparse representation; objective assessment; image reconstruction; visual quality; potential visual artefacts; image denoising; high computational cost; source image patches; compact sub-dictionaries; high-quality image patches; multimodal image sets; signal modelling technique; image representation; learning (artificial intelligence); signal reconstruction requirement
• ISSN: 1751-9659; 1751-9667
• DOI: 10.1049/iet-ipr.2014.0311

In this study, a novel adaptive sparse representation (ASR) model is presented for simultaneous image fusion and denoising. As a powerful signal modelling technique, sparse representation (SR) has been successfully employed in many image processing applications such as denoising and fusion. In traditional SR-based applications, a highly redundant dictionary is always needed to satisfy signal reconstruction requirement since the structures vary significantly across different image patches. However, it may result in potential visual artefacts as well as high computational cost. In the proposed ASR model, instead of learning a single redundant dictionary, a set of more compact sub-dictionaries are learned from numerous high-quality image patches which have been pre-classified into several corresponding categories based on their gradient information. At the fusion and denoising processes, one of the sub-dictionaries is adaptively selected for a given set of source image patches. Experimental results on multi-focus and multi-modal image sets demonstrate that the ASR-based fusion method can outperform the conventional SR-based method in terms of both visual quality and objective assessment.