School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210046, China.
School of Bell Honors, Nanjing University of Posts and Telecommunications, Nanjing 210046, China.
Sensors (Basel). 2023 Jun 21;23(13):5776. doi: 10.3390/s23135776.
This paper introduces a simple but effective image filtering method, namely, local adaptive image filtering (LAIF), based on an image segmentation method, i.e., recursive dilation segmentation (RDS). The algorithm is motivated by the observation that for the pixel to be smoothed, only the similar pixels nearby are utilized to obtain the filtering result. Relying on this observation, similar pixels are partitioned by RDS before applying a locally adaptive filter to smooth the image. More specifically, by directly taking the spatial information between adjacent pixels into consideration in a recursive dilation way, RDS is firstly proposed to partition the guided image into several regions, so that the pixels belonging to the same segmentation region share a similar property. Then, guided by the iterative segmented results, the input image can be easily filtered via a local adaptive filtering technique, which smooths each pixel by selectively averaging its local similar pixels. It is worth mentioning that RDS makes full use of multiple integrated information including pixel intensity, hue information, and especially spatial adjacent information, leading to more robust filtering results. In addition, the application of LAIF in the remote sensing field has achieved outstanding results, specifically in areas such as image dehazing, denoising, enhancement, and edge preservation, among others. Experimental results show that the proposed LAIF can be successfully applied to various filtering-based tasks with favorable performance against state-of-the-art methods.
本文提出了一种简单而有效的图像滤波方法，即基于图像分割方法的局部自适应图像滤波（LAIF）。该算法的灵感来源于这样一种观察，即对于要平滑的像素，仅使用附近相似的像素来获得滤波结果。基于这一观察，在对图像进行平滑处理之前，通过递归膨胀分割（RDS）对相似像素进行分区。具体来说，通过直接以递归膨胀的方式考虑相邻像素之间的空间信息，首先提出 RDS 将导向图像分割成几个区域，使得属于同一分割区域的像素具有相似的属性。然后，在迭代分割结果的指导下，通过局部自适应滤波技术对输入图像进行滤波，该技术通过选择性地对局部相似像素进行平均来平滑每个像素。值得一提的是，RDS 充分利用了包括像素强度、色调信息以及特别是空间相邻信息在内的多种集成信息，从而得到更稳健的滤波结果。此外，LAIF 在遥感领域的应用取得了优异的成果，特别是在图像去雾、去噪、增强和边缘保持等领域。实验结果表明，所提出的 LAIF 可以成功应用于各种基于滤波的任务，性能优于最先进的方法。
