Objective

2

Various inhomogeneous materials

including plastic

wood

ceramic

and other opaque nonconductors with uniform pigmentation

usually present some blocks of reflection areas due to nonuniform illumination conditions. However

these blocks of reflection areas often exert some negative effects on visual quality and degrade the performances of vision algorithms

such as color constancy

image segmentation

visual tracking

and object detection. For simplification

specular components are usually disregarded as outliers by methods based on diffuse component analysis. When we use traditional methods to remove the highlight of a simple image

they cannot completely separate the specular components of the image. For example

the colors and edges of images were always missed

parts of the result image exhibited color distortion

caused artifacts for noisy and highly textured surfaces

the method was inapplicable to natural images. In view of these shortcomings

an improved method of removing highlights based on bilateral filtering is proposed.

Method

2

To solve the shortcomings of the traditional methods

we raise an improved bilateral filtering method. The method comprises three stages. First

we can determine the relationship between the diffuse component and the maximum diffuse chromaticity of pixels in an image according to the dichromatic reflection model and by assuming that illumination chromaticity can be measured (with a white reference). If we want to separate the diffuse component from an image

then we must estimate the maximum diffuse chromaticity value for every pixel. An estimation of the maximum diffuse chromaticity

which is the key to separating specular and diffuse components

is thus necessary. Second

we use an appropriate threshold to divide the input image into two categories. The first class of pixels contains only a diffuse component

whereas the second class of pixels includes specular and diffuse components. We then implement our proposed method (two different algorithms) to estimate the maximum diffuse chromaticity of the two categories. We adopt a compensation function in the second class of pixels. This compensation function can exert a positive effect on pixels with similar RGB channels

as well as avoid the negative effect of the specular component of the second class of pixels. Third

we use the approximated maximum diffuse chromaticity in the second step to guide the smoothing and iteratively apply joint low-pass filter (bilateral filter) to update the maximum diffuse chromaticity estimation. This stage is based on a key observation that the maximum diffuse chromaticity in local patches in colorful images generally changes smoothly. We update the maximum diffuse chromaticity after every iteration. The maximum diffuse chromaticity values can be gradually propagated from diffuse pixels to specular pixels. Our algorithm does not stop until an insignificant difference (set at 0.02 in our experiments) exists between the updated and filtered maximum diffuse chromaticity values. In practice

our method generally converges after two to four iterations. As a result

our method can remove highlights more efficiently than previous methods.

Results

2

In this study

we use classic images with highlights as processing objects and yield a new maximum diffuse chromaticity estimation of pixels that contain specular and diffuse reflections. We also yield a new maximum diffuse chromaticity estimation of remaining pixels that contain only diffuse reflection. With the maximum diffuse chromaticity image as a bilateral filtering guide map

we can remove the specular reflection component

as well as effectively preserve the image edge information. We remove the specular component of the pictures

and the color degradation of pixels with similar values in RGB channels is reduced. In the experiment

the proposed algorithm is compared with the methods of Yang et al. and Shen et al. We compare the peak-signal-to-noise ratios of 50 pictures and determine that our result images increase by 4.17% and 8.40% in average. The processing effect of the proposed algorithm is in line with the human eye vision. The experimental results on synthetic and real images show that our method performs better than the state-of-the-art methods in separating specular reflection.

Conclusion

2

We propose a new estimation of maximum diffuse chromaticity in the highlight removal field. The use of a low-pass filter guarantees the estimated diffuse reflections. With a single color image

the highlight removal problem is formulated as an iterative bilateral filtering process that normally converges in two to four iterations. The experimental evaluation indicates that the proposed method can effectively remove the specular reflection components from images. We also provide an effective theoretical basis for indoor and outdoor images with specular reflection. Our future works involve solving the limitations of our method and practically applying it.