Objective

2

Compared with traditional subjective image enhancement methods

image restoration is an objective method to improve the quality of degraded images on the basis of physical models. The result presents no distortion

low noise

and rich image details. The grade of sinter

which is the main raw material for blast furnace ironmaking

is directly related to the condition of the blast furnace and the output of molten iron. The flame image of the tail section of a sinter machine can fully reflect the sintering state of the material layer

which plays an important role in the detection of the quality of a sintered ore. However

the harsh environment of the sintering machine tail has caused numerous interference factors

such as smoke

dust

and uneven brightness. Such factors lead to the degradation of the flame image of the sintered section collected using a camera. An accurate ambient light value is difficult to obtain by using the dark channel principle alone. This limitation results in halo and distortion. Therefore

obtaining a clear and undistorted cross-section flame image of the tailing layer of the sintering machine is the primary problem in accurately identifying the end point of sintering

and it has an important engineering application value for improving the yield of sintered ore and reducing energy consumption. In this study

a new flame image degradation model for the sintered section is established

and an effective flame image restoration algorithm for the sintered section is proposed.

Method

2

The basis of the image restoration algorithm based on physical model is to establish a degradation model of the image. The atmospheric scattering model proposed by Narasimhan is widely used in the fields of computer vision and image processing. The image degradation model is established based on the atmospheric scattering model; it fully considers the scattering and attenuation characteristics of soot particles in the tail environment of the sintering machine. The model uses a first-order multiscattering method to simplify the multiscattering process of soot and Retinex theory to decompose the image. First

the proposed algorithm calculates the brightness of the original image and adjusts the overall brightness of the image based on the bilateral filtering Retinex method in accordance with the uneven luminance caused by the sintering flame. Second

from the results of image brightness balance

we estimate the ambient light of the sintering machine tail by using the transcendental principle of dark channel and enlarge the image transmissivity distribution with guided filtering. Finally

for the image of the area containing a large region of flame in the smoke environment of the sintering section

we use the tolerance mechanism to improve the transmittance of the flame area and acquire a restored image. This process prevents the color distortion of the restored image and makes the restored image close to the actual situation.

Result

2

In this experiment

a single image collected is used for restoration and subjective evaluation with four other experimental methods. Results of multiscale Retinex restoration show that the method cannot remove the smoke and dust from the flame image of the sintering section

and the noise is excessively high. Compared with the results of multiscale Retinex

the image noise generated using the method with glow and multiple light colors is smaller

but the details of the flame area are unclear and color distortion appears. The results of dark channel priori method experimental restoration improve some details of the flame area but do not eliminate the influence of soot because the flame area causes uneven brightness of the image. This condition leads to the failure of the dark channel priori to estimate the transmission of the image in the dark scene. The experimental results of the method with boundary constraint and contextual regularization present that the image brightness is balanced in a small range

and the details of the flame area are obvious. However

the overall image brightness is high

and the smoke influence remains. The results of this study show that the brightness of the restored image is balanced

the details of the flame area are clear and distinct from the sintering layer

the color is natural without distortion

and the influences of smoke and dust are eliminated. We use a statistical method for image quality evaluation

pixel value variance

average gradient

contrast

information entropy

and peak signal-to-noise ratio as objective evaluation indexes. The experimental results of dark channel priori method have lower statistics than the original image because the method does not address the problem of uneven brightness of the image caused by the sintering flame area. Compared with the statistics of the original image

the statistics of the image obtained using modified method is improved

but the restored image has the problems of noise and color distortion. The algorithm proposed in this study is superior to other methods. The algorithm results maintain a high image contrast. The image information entropy and the peak signal-to-noise ratio are 17.532 bit and 22.127 dB

respectively

which are significantly higher than those of other algorithms.

Conclusion

2

We study a degradation model for the flame image of the tail section of a sintering machine and propose an effective restoration algorithm. We achieve the brightness balance of the restored image and the effect of smoke removal

which are beneficial to the accurate extraction of sintered flame characteristics and lay a foundation for the subsequent identification of the sintering end point.