A novel single image dedusting method based on non-overlap stitching is investigated to solve the issue of image degradation in the non-uniform dusty environment with multiple scattering light. First, the first-order multiple scattering model is used to produce recovered images of the original dusty image by different transmission values. Then each recovered image is classified to clear and dusty regions using an improved dust detection operation by which the clear regions are kept. By repeating the first two steps and by reducing transmission value from 1 to the terminal value optimized by a cost function, series clear regions are produced. Finally, all clear regions with the rest dusty regions in the last recovered image are stitched into a restored image. Experimental results show that this method can effectively remove dust in the image and significantly improve the image quality obviously.
A robust image dehazing algorithm based on the first-order scattering of the image degradation model is proposed. In this work, there are three contributions toward image dehazing: (i) a robust method for assessing the global irradiance from the most hazy-opaque regions of the imagery is proposed; (ii) more detailed depth information of the scene can be recovered through the enhancement of the transmission map using scene partitions and entropy-based alternating fast-weighted guided filters; and (iii) crucial model parameters are extracted from in-scene information. This paper briefly outlines the principle of the proposed technique and compares the dehazed results with four other dehazing algorithms using a variety of different types of imageries. The dehazed images have been assessed through a quality figure-of-merit, and experiments have shown that the proposed algorithm effectively removes haze and has achieved a much better quality of dehazed images than all other state-of-the-art dehazing methods employed in this work.