Image Enhancement and Segmentation Using Wavelet Transform and Histogram Equalization

Image enhancement and segmentation using wavelet transform in comparison with histogram equalization represents a highly effective technical approach. Through wavelet transform implementation - typically using functions like wavedec2() for decomposition and waverec2() for reconstruction in MATLAB - we can enhance image details by processing coefficients at different resolution levels. This technique makes images sharper and more distinct by emphasizing high-frequency components. Meanwhile, histogram equalization, implemented through functions like histeq() or adapthisteq(), increases image contrast by redistributing pixel intensities, thereby making subtle details more prominent. These enhancement and segmentation techniques find applications across multiple domains including medical image processing, image recognition, and computer vision systems. In segmentation, we implement comparative analysis of various methods: threshold segmentation using algorithms like Otsu's method (graythresh()), edge detection through operators such as Canny or Sobel (edge() function), and region growing techniques that group pixels based on similarity criteria. By comparing these techniques through quantitative metrics like PSNR and SSIM, we can select the most appropriate method for specific applications, thereby achieving superior image enhancement and segmentation results. The implementation typically involves MATLAB's Image Processing Toolbox functions for wavelet analysis (wavedec, wrcoef) and segmentation algorithms (imsegkmeans, activecontour).