Natural Image and Video Decomposition with Applications to Single Image Denoising

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-48 Number-3
Year of Publication : 2017
Authors : Deepthi A J, Arun Pradeep
DOI :  10.14445/22312803/IJCTT-V48P128


Deepthi A J, Arun Pradeep "Natural Image and Video Decomposition with Applications to Single Image Denoising". International Journal of Computer Trends and Technology (IJCTT) V48(3):148-159, June 2017. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
Rain, fog and Gaussian noise removal from an image and video is a challenging problem and has been recently investigated extensively. In this paper, present a self-learning based image and video decomposition framework. Based on sparse representation, this method first learns an over-complete dictionary from the high spatial frequency parts of the input image for reconstruction purposes. An unsupervised clustering on the observed dictionary atoms has been performed in this work. And their corresponding reconstructed image versions via, affinity propagation, which allows to identify image-dependent components with similar context information. While applying this method for the applications of image and video denoising, it was able to automatically determine the undesirable patterns like rain streaks, Fog or Gaussian noise. From the derived image components directly from the input image or video, so that the task of single-image denoising can be addressed. DWT is used for better performance. Fog will degrade the quality of the preview image by reducing the saturation and contrast.. The objective of this method is to enhance the visibility, saturation, contrast and reduce the noise in a foggy image. Here single frame is used for enhancing foggy images using multi-level transmission map. The method is fast and free from noise. Comparison with the existing method shows that this method provides better processing time and quality. Experiments were conducted based on: single-image and video denoising with Gaussian, Fog and rain noises. The empirical results confirm the effectiveness and robustness of this approach, which is proved to outperform state-of-the-art image denoising algorithms.

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Denoising, rain removal, fog removal, image decomposition, Natural image, DWT.