Real‐time single image dehazing using block‐to‐pixel interpolation and adaptive dark channel prior

An Effective and Robust Single Image Dehazing Method Using the Dark Channel Prior

“…In Table 5, we give the time consumption comparison of the above methods in Figure 14 (616 × 462), Figure 15 (845 × 496), Figure 16 (291 × 345) and Figure 17 (400 × 600). As we can see, Yu et al's method [25] is slightly faster than ours. However, it is a trade-off between effectiveness and efficiency in nature, since the results of [25] are inferior to our method in terms of both qualitative quality and quantitative results.…”

“…As we can see, Yu et al's method [25] is slightly faster than ours. However, it is a trade-off between effectiveness and efficiency in nature, since the results of [25] are inferior to our method in terms of both qualitative quality and quantitative results. Our method is faster than He et al's [7,26] [17].…”

“…To verify the robustness and effectiveness of our method, we test it on various types of challenging hazy images (including both the synthetic image and the real-world image) and conduct qualitative and quantitative comparisons with several state-of-the-art dehazing methods, such as those by He et al [7], Meng et al [9], Ancuti et al [36], Yu et al [25], Tarel et al [10] and Choi et al [17]. All of the methods are implemented in the MATLAB R2014a environment on an Intel Core i5 2.6-GHz PC with 8 GB RAM.…”

“…The relevant parameters used in the proposed method are demonstrated in Section 3. For fair comparison, we set the parameters used in the other six comparison methods according to [7,9,10,17,25,36].…”

“…Yu et al's method [25] is another improvement of He et al's method [7], and the corresponding dehazing results are demonstrated in Figures 14e, 15e, 16e and 17e. We can notice that this method cannot well handle dense haze image (see Figure 15e); both Figures 14e and 16e are dim and suffer from color distortion; especially, Figure 17e is subjected to serious global color distortion.…”