The necessity to denoisify thermal images dated from decades ago for ecological and scientific purposes, and reduce the hardware costs in the design of the thermal cameras, motivated the present study. We propose, describe and demonstrate the effectiveness of two techniques for reducing considerably different types of noise that thermal images could have, their possibilities for real-time processing, accuracy and easy implementation. The first technique uses the multi-observation for reducing the random noise and identifying the fixed-pattern noise and then, subtracts it from the image. The second technique consists in a neighborhood classification method for identifying the type and degree of the fixed-pattern noise for subtracting it in the correct proportion. Real thermal patterns are used for the analysis. The error analysis is carried out. A novel method for evaluate the quality of the proposed techniques is also presented. The easy practical implementation of these techniques reduces the restrictions to be imposed to the sensor's hardware, reducing the sensor's production costs.