Objective

2

This study proposes a detail-aware texture removal algorithm based on existing studies. When removing image textures

the proposed method maintains the fine structural information of the image

particularly the special details (e.g.

slender structure and corner information) that are easily obscured in other methods. With the continuous development of computer technology

the application of image processing technology has become increasingly widespread in pattern recognition

security monitoring

smart driving

computer photography

and other areas. However

the image quality obtained directly from the image acquisition card is not satisfactory. Therefore

image preprocessing is necessary. Texture filtering is an important step in image preprocessing. The image edge in a texture image is the main component of the image structure. Traditional image filter processing techniques

such as median and Gaussian filtering

can filter noise to a certain extent

but the structure context is also filtered. Therefore

this study investigates how to remove texture and maintain the slender structure context simultaneously.

Method

2

The main idea of the proposed algorithm is to compute the optimal filter scale by leveraging a novel slender structure recognition technique and an improved structure angle relative total variation based on multiple directions and obtain the filtering result through a guided filter. The method consists of four steps. First

to address the deficiency of existing algorithms

this study proposes a method that can identify and enhance slender structures to avoid smoothing them in the subsequent texture filtering process. Second

to estimate the optimal filtering kernel scale of each pixel

the original relative total variation model is improved by searching the minimum relative total variation in multiple directions so that it can distinguish textures and boundaries effectively

and corner information is effectively distinguished from texture. Then

the detected elongated structure is normalized to the improved metric of relative total variation

and the filtered kernel scale is estimated to generate a guided filtered image. Thus

large-scale filtering kernels are used in flat or textured regions

and the filtering kernels are reduced near the edges and corners of the structure. Finally

a texture-removed image is obtained by combining the joint bilateral filters.

Result

2

We evaluate our method on different types of pictures

including mosaics and paintings. Experiments are conducted on a Windows 8.1 operating system

and the proposed method is implemented in MATLAB language. No reasonable quantitative objective evaluation metrics exist in the research field of texture filtering; thus

subjective evaluation by human eyes is commonly used. In the experiments

we compare five existing methods of texture filtering

namely

bilateral texture filtering

rotation guided filtering

relative total variation

scale-sensitive structural protection filtering

and interval gradient operator. Compared with these methods

the proposed algorithm needs a slightly longer computing time. Specifically

for an image with 394×304 pixels

the proposed method consumes 3.37 s

whereas bilateral texture filtering

rotation guided filtering

relative total variation

scale-sensitive structural protection filtering

and interval gradient operator consume 2.23

0.07

0.23

1.01

and 3.29 s

respectively. Our method outperforms these methods in terms of texture removal while maintaining slender structures and corner details. We also analyze the iteration number and parameter value standard deviation (σ) of the proposed algorithm. The comparative experiments demonstrate that with one iteration used to remove texture

the result of our algorithm is better than those of relative total variation and interval gradient operator methods. A large σ is selected when the optimal filter scale is large

and a small σ is equipped when the optimal filter scale is small.

Conclusion

2

The texture filter designed in this study performs well in maintaining features

such as elongated structures and sharp corners in an image after texture removal

thus providing a powerful image preprocessing method for image subsequent processing

including image detail enhancement

edge detection

image abstraction

and image segmentation. For the problem of sharp reduction in the filter kernel scale encountered in the experiment

this study provides a reasonable explanation and solution. The proposed algorithm is limited by its long computing time. In general

the proposed algorithm obtains better results compared with others despite its slightly lower computing time efficiency.