Xu Huiqin, Zhang Zhongrong, Gao Yin, Liu Haizhong, Xie Feng, Li Jun
School of Mathematics and Physics, Lanzhou Jiaotong University, Lanzhou, China.
Fujian Science & Technology Innovation Laboratory for Optoelectronic Information of China, Fuzhou, China.
Front Neurorobot. 2022 Jun 27;16:729924. doi: 10.3389/fnbot.2022.729924. eCollection 2022.
The biggest challenge of texture filtering is to smooth the strong gradient textures while maintaining the weak structures, which is difficult to achieve with current methods. Based on this, we propose a scale-adaptive texture filtering algorithm in this paper. First, the four-directional detection with gradient information is proposed for structure measurement. Second, the spatial kernel scale for each pixel is obtained based on the structure information; the larger spatial kernel is for pixels in textural regions to enhance the smoothness, while the smaller spatial kernel is for pixels on structures to maintain the edges. Finally, we adopt the Fourier approximation of range kernel, which reduces computational complexity without compromising the filtering visual quality. By subjective and objective analysis, our method outperforms the previous methods in eliminating the textures while preserving main structures and also has advantages in structure similarity and visual perception quality.
纹理滤波的最大挑战在于在保持微弱结构的同时平滑强梯度纹理，而当前方法难以做到这一点。基于此，我们在本文中提出了一种尺度自适应纹理滤波算法。首先，提出了利用梯度信息进行的四方向检测用于结构测量。其次，基于结构信息获得每个像素的空间核尺度；较大的空间核用于纹理区域的像素以增强平滑度，而较小的空间核用于结构上的像素以保持边缘。最后，我们采用距离核的傅里叶近似，这在不影响滤波视觉质量的情况下降低了计算复杂度。通过主观和客观分析，我们的方法在消除纹理同时保留主要结构方面优于先前方法，并且在结构相似性和视觉感知质量方面也具有优势。
