Image Enhancement Techniques: Grayscale Transformation, Spatial Domain Filtering, Frequency Domain Enhancement, Color Enhancement, and Wavelet Enhancement

Image enhancement refers to a series of processing methods that improve image quality and visual effects. Grayscale transformation enhancement adjusts the gray levels of an image to enhance contrast and brightness, typically implemented using functions like histogram equalization or gamma correction in libraries such as OpenCV or MATLAB. Spatial domain filtering enhancement applies various filters to modify image characteristics in the spatial domain, enhancing details and sharpness through techniques like convolution with kernels for blurring, sharpening, or edge detection. Frequency domain enhancement involves transforming images into the frequency domain using Fourier transforms and applying frequency filters to enhance specific frequency components, such as high-pass filters for edge emphasis or low-pass filters for noise reduction. Color enhancement improves color effects by adjusting saturation and hue, often achieved through color space transformations like HSV manipulation. Wavelet enhancement is a method based on wavelet transforms that decomposes images into wavelet coefficients at different scales, enhancing these coefficients to improve image quality and detail visibility, commonly implemented using discrete wavelet transform algorithms.