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When the noise and blur parameters are known, the noise and blur interference can be largely eliminated in the haze removal, and the efficiency of the proposed haze removal algorithm can therefore be better demonstrated.
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In this study, a novel single-image based dehazing framework is proposed to remove haze artifacts from images through local atmospheric light estimation.
Only the proposed method removes haze considering sensor blur; thus, the result in Figure 6d is better than the ones obtained by the methods of Fattal [10] and Milanfar [22] in Figure 12c,d, respectively.
Since the computational complexity of the low-rank technique is low, it is shown that the proposed approach for haze removal is fast, and can even achieve better results than the state-of-the-art methods in a single image dehazing.
This limitation was overcome by He et al. [14], who proposed an automatic haze-removal approach for single images using a dark channel as prior.
Recently, Matlin and Milanfar [22] have proposed the removal of haze and noise from a single image by the use of the BM3D denoising algorithm and an iterative regression method.
Again, it can be seen that the proposed method effectively recovers haze-free images.
Despite the haze surrounding the proposed agreement, some of the most alarmist speculation appears to have been exaggerated.
And Figure4 demonstrates that a comparison of visibility enhancement results from a color image with high-density haze between the proposed algorithm and the two popular methods[20, 21].
Based on this model, a three-stage haze removal algorithm is proposed.
Based on this degradation model, a three-stage haze removal algorithm is proposed.
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