Image smoothing is by reducing pixel-wise gradients to smooth out details. As existing methods always rely on gradients to determine smoothing manners, it is difficult to distinguish structures and details to handle distinctively due to the overlapped ranges of gradients for structures and details. Thus, it is still challenging to achieve high-quality results, especially on preserving weak structures and removing high-contrast details. In this paper, we address this challenge by improving the real-time optimization-based method via iterative least squares (called ILS). We observe that 1) ILS uses gradients as the independent variable in its penalty function for determining smoothing manners, and 2) the framework of ILS can still work for image smoothing when we use some values instead of gradients in the penalty function. Thus, corresponding to the properties of pixels on structures or not, we compute some values to use in the penalty function to determine smoothing manners, and so we can handle structures and details distinctively, no matter whether their gradients are high or low. As a result, we can conveniently remove high-contrast details while preserving weak structures. Moreover, such values can be adjusted to accelerate optimization computation, so that we can use fewer iterations than the original ILS method for efficiency. This also reduces the changes onto structures to help structure preservation. Experimental results show our advantages over existing methods on efficiency and quality.

翻译：图像平滑通过减少像素梯度来平滑细节。由于现有方法通常依赖梯度来决定平滑方式，而结构梯度和细节梯度范围重叠，因此难以区分结构和细节并分别处理。这导致实现高质量结果仍具挑战性，尤其在保留弱结构和去除高对比度细节方面。本文通过改进基于迭代最小二乘（ILS）的实时优化方法来解决这一挑战。我们观察到：1) ILS在惩罚函数中使用梯度作为自变量以决定平滑方式；2) 当在惩罚函数中用某些值替代梯度时，ILS框架仍能用于图像平滑。因此，根据像素是否属于结构区域，我们计算相应值并用于惩罚函数以决定平滑方式，从而能够区分处理结构和细节，无论其梯度高低。这样，我们能在保留弱结构的同时便捷地去除高对比度细节。此外，这些值可调整以加速优化计算，从而比原始ILS方法使用更少迭代次数以提高效率。这还减少了对结构的改动，有助于结构保持。实验结果表明，本方法在效率和质量上均优于现有方法。