Abstract: The temperature distribution of pig skin is an important indicator to characterize its physiological state and disease. However, due to the surface hair coat, the skin temperature accuracy which detected by infrared thermography (IRT) is affected and its ability to diagnosis of fever and disease is reduced. The purpose of this paper is to explore the influence patterns of the coat on the skin temperature distribution and propose the thermal image processing method to eliminate the influence of the coat on temperature accuracy. The animals for experimental data were 12 sows in empty pregnant period with the average ambient temperature of 27.4 ℃ and humidity in the piggery of 80.3% respectively. The body surface temperature was measured by hand-held infrared thermal imager (Fluke, Ti 300) with a resolution of 240 pixels×180 pixels and sensitivity of 50 mK. And it also carried a laser distance measuring sensor with a resolution of 0.01 m to measure the distance between the measured object and the thermal imager. The statistics of the temperature distribution detected by IRT from the region of interest (ROI) under normal coat (NC) was compared to that under shed coat (SC) state. The statistical data indicated that the hair coat produced a large number of “canyon”-like low temperature noise in temperature distribution in NC state, which reduced the minimum temperature and average temperature of the ROI, but had no significant effect on the maximum temperature with diagnostic ability. According to the noise distribution characteristics and the influence pattern, an image noise filtering algorithm named the grid maximum value bicubic interpolation filter (GMBI) was proposed. The GMBI algorithm consisted of three steps including image mesh segmentation, filtering with maximum value and image bicubic interpolation. The key problem of GMBI was how to select the appropriate neighborhood size to ensure that each block contained at least one skin temperature value and the resolution was as high as possible. In this study, mathematical statistics was employed and it was found out that the optimal neighborhood size was 4.25 mm. In order to evaluate the validity of the algorithm quantitatively, the mean square error (MSE), peak signal-to-noise ratio (PSNR) and the difference of maximum, minimum and mean between the processed images by GMBI and the SC thermal images were calculated. The experimental data showed that the differences of minimum and average were greatly reduced from the original 1.59 and 0.47 to 0.13 and 0.07 ℃ (P<0.01), which both were within the maximum allowable error range(±0.3 ℃) for disease diagnosis. Moreover, the MSE decreased from 0.38 to 0.05 (P<0.01), while PSNR increased from 45.14 dB to 53.66 dB. In conclusion, the GMBI purposed in this study can filter the majority of noise caused by hair in temperature distribution and significantly improve skin temperature detection accuracy.