2 research outputs found
Quantitative Evaluation of Base and Detail Decomposition Filters Based on their Artifacts
This paper introduces a quantitative evaluation of filters that seek to
separate an image into its large-scale variations, the base layer, and its
fine-scale variations, the detail layer. Such methods have proliferated with
the development of HDR imaging and the proposition of many new tone-mapping
operators. We argue that an objective quality measurement for all methods can
be based on their artifacts. To this aim, the four main recurrent artifacts are
described and mathematically characterized. Among them two are classic, the
luminance halo and the staircase effect, but we show the relevance of two more,
the contrast halo and the compartmentalization effect. For each of these
artifacts we design a test-pattern and its attached measurement formula. Then
we fuse these measurements into a single quality mark, and obtain in that way a
ranking method valid for all filters performing a base+detail decomposition.
This synthetic ranking is applied to seven filters representative of the
literature and shown to agree with expert artifact rejection criteria.Comment: 12 pages; 11 figures; 2 tables; supplementary material available
(link given in the paper
Real-time Image Smoothing via Iterative Least Squares
Edge-preserving image smoothing is a fundamental procedure for many computer
vision and graphic applications. There is a tradeoff between the smoothing
quality and the processing speed: the high smoothing quality usually requires a
high computational cost which leads to the low processing speed. In this paper,
we propose a new global optimization based method, named iterative least
squares (ILS), for efficient edge-preserving image smoothing. Our approach can
produce high-quality results but at a much lower computational cost.
Comprehensive experiments demonstrate that the propose method can produce
results with little visible artifacts. Moreover, the computation of ILS can be
highly parallel, which can be easily accelerated through either multi-thread
computing or the GPU hardware. With the acceleration of a GTX 1080 GPU, it is
able to process images of 1080p resolution () at the rate of
20fps for color images and 47fps for gray images. In addition, the ILS is
flexible and can be modified to handle more applications that require different
smoothing properties. Experimental results of several applications show the
effectiveness and efficiency of the proposed method. The code is available at
\url{https://github.com/wliusjtu/Real-time-Image-Smoothing-via-Iterative-Least-Squares}Comment: 24 pages, accepted to ACM Transactions on Graphics, presented at
SIGGRAPH 202