Spatially guided local Laplacian filter for nature image detail enhancement

Nature images make up a significant proportion of the ever growing volume of social media. In this context, automatic and rapid image enhancement is always among the favorable techniques for photographers. Among the image representation models, the Gaussian and Laplacian image pyramids based on isotropic Gaussian kernels were once considered to be inappropriate for image enhancement tasks. The recently proposed Local Laplacian Filter (LLF) updates this view by designing a point-wise intensity remapping process. However, this model filters an image with a consistent strength instead of a dynamical way which takes image contents into account. In this paper, we propose a spatially guided LLF by extending the single-value key parameter into a multi-value matrix that dynamically assigns filtering strengths according to image contents. Since it is still very challenging to recognize arbitrary image contents with machine learning methods, we propose a simple but effective technique, which only approximates the richness of image details instead of specific contents. This trade-off between concrete semantics and algorithm efficiency enables filtering strengths to be spatially guided in the LLF process with little extra computational cost. Experimental results validate our method in terms of visual effects and a conditionally faster LLF implementation.