The use of noncontact temperature measurements based on colored charge-coupled device (CCD) has significantly accelerated in recent years. However

the quality of radiation images is degraded by ambient light

smog

and dust interferences

and their suppression is indispensable to facilitate accurate measurements. Noise reduction and image target segmentation are regarded as key steps in colored CCD-based colorimetric thermometry. Nevertheless

traditional color image-processing methods are unsuitable for radiation images. In this work

an approach to the problem of impulsive noise removal and target segmentation in radiation images is presented. Given the strong spatial correlation among adjacent pixels in radiation images

a normalized spatial distance weighted function is designed to quantify the correlation degree among pixels in various distances. A normalized spatial distance weighted directional-distance filter is built based on the spatial distance weighted function and the directional-distance filter for the removal of color and light noises. In traditional filters

such as vector median

basic vector directional

and directional-distance filters

only the angle or distance among vectors is utilized. By contrast

the spatial distance of the vectors in a filtering window is considered in the normalized spatial distance weighted filter to alleviate the problems caused by the blurring properties of traditional filters. In radiation images

the blue color is nearly zero

whereas the red and green colors are distributed along a certain line with the double peak phenomenon. Traditional image segmentation algorithms fail because of the missing blue color information. In the proposed segmentation algorithm

the red and green two-dimensional color vectors are reduced to one dimension based on the optimal one-dimensional projection using the Fisher criterion. The measured target is segmented in the one-dimensional projection using Otsu’s method. The segmentation approach utilizes the red and green color information to conquer the interference with light that is similar to the target. The proposed method is compared with the traditional method of directional-distance filter and the clustering algorithm in color space. In a high-temperature heating furnace

the maximum absolute error is 1.99% using the traditional method

whereas the maximum absolute error decreases to 1.10% using the proposed method. In a copper-matte converting flash furnace

the maximum absolute errors are 3.67% and 1.31% using the traditional method and proposed method

respectively. A set of colored CCD-based colorimetric thermometry is designed by the proposed image-preprocessing method. The thermometry performance is examined with the blackbody furnace at the Henan Institute of Metrology. Experimental results show that the maximum absolute error is 4.2℃

and the maximum relative error is 0.43% of the thermometry in the measured range of 800℃~1 520℃. The main advantage of the proposed normalized spatial distance weighted filter is its ability to suppress the noise component

while preserving image details. The proposed image segmentation approach overcomes the interferences whose brightness is close to the target image and segments the target accurately. The proposed radiation image-preprocessing method is characterized by low computational complexity

which enables the adaption of the novel technique in real-time temperature measurement.