Comparative Analysis of Different Interpolation Techniques for Low Resolution Thermal Image from Thermopile Sensor

Image processing and analysis make extensive use of image scaling. In digital image processing, resizing an image is referred to as image scaling. When images are magnified, one of the most important factors is their resolution. There have been several interpolation techniques proposed to enhance the image quality. This paper's primary objective is to provide a comparative analysis of different interpolation methods like nearest neighbor, bilinear, and bicubic interpolation for thermal images in real-time. It is observed from the result that by using the bicubic interpolation method the average Peak Signal to Noise Ratio (PSNR) is 29dB which is high by 1 dB compared to bilinear and nearest neighbor interpolation methods. The Mean Square Error (MSE) value is 26 which is less by approximately 4 compared to bilinear and nearest neighbor interpolation methods. The research work is carried out by reading the thermal images from the thermopile sensor using Raspberry Pi in real time.