Two‐stage image smoothing based on edge‐patch histogram equalisation and patch decomposition

Abstract

Part of important structural edges in the image is smoothed due to the small gradients, while the others are preserved with greater gradients. Therefore, the authors propose a two‐stage image smoothing method based on edge‐patch histogram equalisation and patch decomposition. The authors' purpose is to increase the gradient of important structural edges while reducing the gradient of the texture region. Therefore, they divide the image into edge‐patches where the structural edges are concentrated or non‐edge‐patches where the texture details are concentrated by image segmentation. The edge‐patch needs to be equalised by the histograms for increasing the gradient of the edge pixels. All patches are decomposed to extract the smooth component for reducing the gradient of pixels. The smooth component of each patch is smoothed via L0 gradient minimisation. In order to ensure the continuity of the patch boundaries, the edge‐patch is inversely equalised. Finally, the whole image is smoothed via L0 gradient minimisation for removing residual textures and seams. Experimental results demonstrate that the proposed method is more competitive in maintaining important structural edges and removing texture details than the state‐of‐the‐art approaches. The proposed method can be applied to many areas of image processing.

Construction of Computer Microscope Image Segmentation Model Based on Fourth-Order Partial Differential Equation Smoothing

“…The P-M method does not have the blurring and "boundary drift" of Gaussian filtering, nor does it exist the pseudoedge of formula (2), it also has a strengthening effect on some features (such as brain outline), and the effect is ideal.…”

“…Image enhancement and restoration are used in archaeology, physics, and other fields. On the other hand, image processing helps to solve the problem of machine perception, that is, extracting information from images that is more suitable for computer processing; the application fields include automatic character recognition, industrial machine vision for production inspection, military identification, automatic fingerprint processing, X-ray processing, radiation and blood sample classification processing, and aerial and satellite image processing [ 2 ]. Therefore, in order to complete high-level computer vision tasks, the accurate acquisition of images and the proper representation of image visual information are the basic problems of image processing, which have very important theoretical significance and practical value.…”

Construction of Biologic Microscopic Image Segmentation Model Based on Smoothing of Fourth-Order Partial Differential Equation

“…However, it is necessary to update a histogram with a large color change in real time and calculate the Euclidean distance between the reference model and the reference model, which requires a large amount of calculation and is difficult to ensure the segmentation efficiency and robustness to the light [ 6 ]. Liu et al used Delaunay triangulation to divide the cell image into irregular triangles [ 7 ]. Chen et al noted that in addition to dividing the cell image horizontally and vertically, the image was also split in two at 45 and 275 directions [ 8 ].…”

A Fast Point Cloud Ground Segmentation Approach Based on Coarse-To-Fine Markov Random Field

“…After initializing the MRF model, we define the regional term R (A) and the boundary term B (A) of the energy function of MRF model based on the distribution characteristics of the point cloud. Referring to the application of the histogram in image processing tasks [27], the height of the point cloud is enlarged proportionally and then rounded to g(A p ),…”