Conversion Program from Grayscale Images to RGB Pseudo-color Images

This program demonstrates a complete workflow for converting grayscale images into RGB pseudo-color images. The implementation begins with essential preprocessing steps including noise reduction using filters like Gaussian or median filters, and contrast enhancement through techniques such as histogram equalization or CLAHE (Contrast Limited Adaptive Histogram Equalization). Following preprocessing, we apply various color mapping algorithms where each pixel's intensity value is transformed into RGB color values. Key algorithms implemented include linear mapping (using interpolation between predefined color points) and nonlinear mapping (employing mathematical functions like logarithmic or exponential transformations). The program utilizes lookup tables (LUTs) for efficient color conversion. Finally, post-processing techniques are applied to enhance visual quality, including median filtering for noise reduction and edge-preserving filters like bilateral filtering to maintain important image structures. Through this implementation, you'll learn fundamental image processing techniques for creating visually appealing pseudo-color images, with code examples demonstrating parameter tuning for different image characteristics.