(a) No detail increase (a = 1) (b) Detail increased (a = 0.25)

Figure 14. Our approach is purely signal-based and its ignorance of scene semantics can lead to artifacts. For a large increase in local contrast (b), at a level similar to Figure 12, the sky gets locally darker behind clouds, because it forms a blue-0white texture amplified by our filter. Our result for this example is good elsewhere, and this issue does not appear with a more classical rendition (a).

Less details More details
a = 0.25 a = 0.5 a = 2 a = 4

s = 0 . 1

s = 0 . 2

s

=

0

. 4

Figure 15. Our filter to enhance and reduce details covers a large space of possible outputs without creating halos. Many problems related to photo editing are grounded in these properties of images and we believe that a better understanding can have benefits beyond the applications demonstrated in this paper.

6. CONCLUSION

Link to recent work. We first presented this work at the
ACM SIGGRAPH conference in 2011. The main difference
with our original article is Section 3 that now focuses on
qualitative properties of edges. A formal discussion of these
properties can be found in Paris et al. 28 Since then, we also
extended this work with a fast algorithm that makes Local
Laplacian Filters practical, an analysis that shows their rela-
tionship to the Bilateral Filter, an application to the transfer
of gradient histograms applied to photographic style trans-
fer, and additional comparisons with existing techniques
such as the Guided Filter. 17 These results are described in
Aubry et al. 2

Although Local Laplacian Filters can reduce image details, Xu et al. 39, 40 have shown that they do not fully remove them and have proposed filters that completely suppress details for applications such as cartoon rendering and mosaic texture removal. By addressing the extreme detail removal problem, this work is complementary to Local Laplacian Filters that perform well at extreme detail increase. Hadwiger et al. 16 have introduced a dedicated data structure to process very large images efficiently and have demonstrated its application to Local Laplacian Filtering.

Closing note. We have presented a new technique for edge-aware image processing based solely on the Laplacian pyramid. It is conceptually simple, allows for a wide range of edge-aware filters, and consistently produces artifact-free images. We demonstrate high-quality results over a large variety of images and parameter settings, confirming the method’s robustness. Our results open new perspectives on multiscale image analysis and editing since Laplacian pyramids were previously considered as ill-suited for manipulating edges. Given the wide use of pyramids and the need