Image Contrast Enhancement Using Gray-Level Stretching

The primary tool used for image contrast enhancement processing is MATLAB. In this process, we adopt the gray-level stretching method. Gray-level stretching enhances image contrast by adjusting the pixel value range of the image. This method can be implemented using MATLAB's image processing functions such as imadjust() or by creating custom stretching algorithms. The gray-level stretching algorithm typically involves mapping the original pixel values to a new range using linear or non-linear transformation functions. For example, a basic linear stretch can be implemented by: 1. Calculating the minimum and maximum pixel values in the original image 2. Applying the transformation: new_value = (original_value - min_value) * (new_max - new_min) / (max_value - min_value) + new_min This approach makes image details more distinct and accentuates differences between various regions. Through MATLAB-based image enhancement processing, particularly using the gray-level stretching method, we can effectively improve image quality and enhance visual perception. The implementation may involve histogram analysis and parameter adjustment to optimize the stretching effect for different image characteristics. Key MATLAB functions relevant to this process include: - imread() for loading images - imadjust() for contrast adjustment - stretchlim() for automatically determining stretch limits - imshow() for displaying results - histeq() for histogram equalization as an alternative approach The gray-level stretching method is particularly effective for images with poor contrast where the pixel values occupy only a small portion of the available dynamic range.