Fast Multilayer Laplacian Enhancement

摘要: A novel, fast, and practical way of enhancing images is introduced in this paper. Our approach builds on Laplacian operators well-known edge-aware kernels, such as bilateral nonlocal means, extends these filter's capabilities to perform more effective fast image smoothing, sharpening, tone manipulation. We propose an approximation the Laplacian, which does not require normalization kernel weights. Multiple Laplacians affinity weights endow our method with progressive detail decomposition input from fine coarse scale. These components are blended by a structure mask, avoids noise/artifact magnification or loss output image. Contributions proposed existing editing tools are: 1) low computational memory requirements, making it appropriate for mobile device implementations (e.g., finish step camera pipeline); 2) range filtering applications enhancement denoising only few control parameters, enabling user apply combination various (and even opposite) effects.