Table of Contents
Journal of Computational Engineering
Volume 2014, Article ID 125356, 9 pages
Research Article

An Image Dehazing Model considering Multiplicative Noise and Sensor Blur

1Department of Mathematical and Computational Sciences, National Institute of Technology, Karnataka, Srinivasanagar, Mangalore 575025, India
2Department of Electronics and Communication Engineering, National Institute of Technology, Karnataka, Srinivasanagar, Mangalore 575025, India

Received 15 September 2014; Revised 25 November 2014; Accepted 28 November 2014; Published 22 December 2014

Academic Editor: Quan Yuan

Copyright © 2014 P. Jidesh and A. A. Bini. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A restoration model considering the data-dependent multiplicative noise, shift-invariant blur, and haze has been introduced in this paper. The proposed strategy adopts a two-step model to perform a single image dehazing under the blurred and noisy observations. The first step uses the well-known dark channel prior method to estimate the transmission of the medium and atmospheric light that signifies the global color of the haze and dehaze the images. The second step performs denoising and deblurring under a Gamma distributed noise setup and a linear blurring artefact. The restoration under the above mentioned setup has quite a few applications in satellite and long-distant telescopic imaging systems, where the captured images are noisy due to atmospheric pressure turbulence and hazy due to the presence of atmospheric dust formation; further they are blurred due to the common device artefacts. The proposed strategy is tested using a large amount of available image-sets and the performance of the model is analysed in detail in the results section.