A novel algorithm for fast compression and reconstruction of infrared thermographic sequence based on image segmentation

• A novel algorithm for compression and reconstruction of infrared thermal image sequence is proposed.
• Image segmentation is utilized to the compression of the thermal image sequence in space dimension.
• Thermal image sequences of two embedded defective specimens made of different materials are successfully tested.
• A high compression ratio is comfortably achieved, and the speed is remarkably improved.

High resolution in space and time, which is associated with massive data processing and storage, is becoming the new trend of thermographic non-destructive inspection application. Therefore, it is demanding to develop a fast and precise data post-processing technique for high resolution thermal images. Through analyzing the attenuation of thermal wave temperature and the morphology feature of thermograph, the authors propose a novel algorithm, which provides an effective way to improve the precision and computing speed for post-processing of thermal image data, in this paper. Firstly, the algorithm will sort the thermographic sequence data in space by using image segmentation method. Secondly, the algorithm will employ classical fitting calculation to fit all the temperature decay curves. At last, the algorithm will use the fitting parameters of the curves as the parameters for compression and reconstruction of thermographic sequences. The proposed algorithm manages to compress thermographic sequences in time and space simultaneously. To validate the proposed algorithm, the authors used two defective specimens which were made of different materials to conduct the experiment. The experimental results showed that the proposed infrared thermographic sequence compression and reconstruction algorithm is an effective solution with high speed and high precision. Compared to the conventional method, this algorithm is not only noise resistant in time domain but also can increase the computing speed by hundreds of times.