Natural scenes generally contain different scale objects and textures

which carry rich information in regard to human perception.Texture usually signifies pixel values

which change with high frequency.Generally

images are composed of many important structures

texture

edges

etc.Therefore

mining the meaningful structure from textures or complex background images is a critical task in vision processing.The core of image smoothing lies in the separation of structure and texture.Effective preservation of the structure while suppressing the texture with strong gradient or varying scales is a challenging problem.Most of the existing image smoothing methods tends to deal with weak gradient texture images;if the texture gradient is strong

then these methods will fail.To solve the abovementioned problem

a structure recognition guided texture smoothing algorithm is proposed

which deals with the structure and the texture separately and detect structure before image smoothing. First

this paper argues that the fundamental difference between structure and texture is the repetition pattern.Particularly

the structure should be sparse and the texture should be a region with a repeating pattern.According to this characteristic

the discriminative features for distinguishing between structure and texture are designed and extracted based on the multi-scale analysis of inherent variation.At least two reasons are available for presenting the multi-scale approach.One reason is that structure and texture are relative.When the scale is small

the texture may not show up

and thus the scale needs to be enlarged and the essence of the texture is released.The other reason is that the texture in the image is diverse

and the adaptive scale in different regions is difficult.Furthermore

textures with various attributes may exist in the same image

a single scale can only solve the partial texture with the default scale parameter and the recognition of other textures will lose.Therefore

multi-scale analysis of inherent variation is proposed to ensure that different textures can display their own repetitive pattern attributes.Second

the core part in the field of pattern recognition is feature extraction.Therefore

the feature extracted must be more robust to guarantee the discrimination ability is strong enough and the stability is good enough.To obtain more accurate features

we need to consider the multi-scale inherent variation in the macroscopic view and grasp its general rules.After we analyze the trend of multi-scale inherent variation curves at different pixel locations

several discriminative features are extracted.Then

these features can be used for subsequent structural recognition.We regard the separation of texture and structure as a typical two-class issue

and the support vector machine is a classical two-class classification method.Compared with many existent machine learning methods

it is a relatively lightweight classifier

which can obtain desirable classification results without a large sample.Consequently

this paper prefers to use the support vector machine to distinguish the texture and structure

with the help of support vector machine

a classifier is trained with the extracted feature pixels

and utilized to classify structure and non-structure pixels efficiently.However

due to the block effect in edge compression and the computational mechanism of inherent variation

pixels nearby the structure will always be affected by the real structure and its multi-scale inherent variation curve is similar in structure.Hence

the support vector machine classification results cannot reach a single pixel.We observed large amounts of data and find that the non-structured pixel appeared symmetrically on both sides of the window.Although the support vector machine classification results are coarser

in the middle of the skeleton should be considered as the real structure.In this paper

a morphological thinning method is adopted directly to get a thinner structure

but the results of thinning still have some weakness.To dispose the shortcomings of the support vector machine classification results after thinning operation

we design two steps of post-processing work

including outlier rejection and deburring

which solve the burr and mistaken isolate.As such

the finer structure recognition maps can be obtained.Finally

based on the fine structure obtained in the previous step

a structure guided bilateral image smoothing method is put forward to remove texture while preserving structure. The multi-scale inherent variation features proposed in this paper achieve a correct rate of 96.12% with support vector machine

and our structure guided image smoothing results can effectively suppress the texture details with strong gradient or varying scales while preserving the structure.These excellent experimental results are compared to some results of previous methods

which reveal that the proposed methodology yields better image smoothing. In view of the limitations of existing similar methods

this paper analyzes the characteristics of inherent variation deeply and proposes an algorithm to distinguish the structure and texture by means of multi-scale inherent variations.Based on the support vector machine classification results

a post-processing is used to obtain a finer structure recognition map.Then

a structure guided bilateral image smoothing method is applied to remove texture while preserving structure.Our algorithm outperforms the state-of-the-art image smoothing methods

especially for those images containing texture with strong gradient or varying scales

which could strongly promote such technical fields as structure extraction

detail enhancement

image segmentation

tone mapping

image fusion

and object recognition

which reflect the potential practical application values.