基于峰值感知和多尺度约束的加权引导滤波器

Weighted guided filter based on peak-aware and multi-scale constraints

引导图像滤波无法保留锐利边缘,导致平滑图像出现结构过度模糊问题,为此提出新的加权引导图像滤波器.利用峰值感知加权提取图像的边缘和结构信息,通过多尺度约束提高所提滤波器的鲁棒性.基于图像方差信息将滤波损失函数的正则化项系数改进为自适应式.边缘感知平滑、图像细节增强、纹理去除平滑以及图像去噪领域的应用实验结果表明,所提滤波器在视觉效果、峰值信噪比以及结构相似度上均优于参与对比的引导图像滤波器.与边缘感知平滑实验中次优滤波器的峰值信噪比和结构相似度相比,所提滤波器的峰值信噪比平均高2.62 dB,结构相似度平均高0.0286.

A new weighted guided image filter was proposed for the problem that guided image filtering fails to preserve sharp edges and leads to excessive structural blurring in smoothed images.Peak-aware weighting was utilized to extract edge and structural information from images,and the robustness of the proposed filter was improved by multi-scale constraints.The regularization term coefficients of the filter loss function were improved to an adaptive form based on the image variance information.Application experiments were carried out in edge-aware smoothing,image detail enhancement,texture removal smoothing,and image denoising,and the results showed that the proposed filter outperformed the guided image filters involved in the comparison in terms of visualization,peak signal-to-noise ratio,and structural similarity.Compared with the peak signal-to-noise ratio and structural similarity of the suboptimal filters of the edge-aware smoothing experiments,the peak signal-to-noise ratio was 2.62 dB higher on average,and the structural similarity was 0.0286 higher on average.