平滑的
缩小
计算机科学
人工智能
图像(数学)
计算机视觉
算法
程序设计语言
作者
Xu Li,Cewu Lu,Yi Xu,Jiaya Jia
标识
DOI:10.1145/2070752.2024208
摘要
We present a new image editing method, particularly effective for sharpening major edges by increasing the steepness of transition while eliminating a manageable degree of low-amplitude structures. The seemingly contradictive effect is achieved in an optimization framework making use of L0 gradient minimization, which can globally control how many non-zero gradients are resulted in to approximate prominent structure in a sparsity-control manner. Unlike other edge-preserving smoothing approaches, our method does not depend on local features, but instead globally locates important edges. It, as a fundamental tool, finds many applications and is particularly beneficial to edge extraction, clip-art JPEG artifact removal, and non-photorealistic effect generation.
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