Infrared Image Processing

When processing infrared images, we need to consider methods to enhance image clarity and analytical effectiveness. To achieve this, we can implement various computational techniques to emphasize prominent features in the images. For instance, we can apply spatial or frequency domain filters (such as Gaussian or median filters) to reduce noise interference, or utilize edge detection algorithms (like Sobel, Canny, or Laplacian operators) to identify object contours within the image. Additionally, we can employ histogram equalization techniques to redistribute pixel intensity values, thereby increasing image contrast and making features more distinct. Through the implementation of these algorithms and corresponding MATLAB/Python functions (e.g., imfilter(), edge(), histeq() in MATLAB or OpenCV's filter2D(), Canny(), equalizeHist()), we can effectively process infrared images, subsequently improving analysis efficiency and accuracy. The code implementation typically involves loading images using imread(), preprocessing with appropriate filters, applying feature enhancement algorithms, and finally visualizing results with imshow() functions.