MATLAB Implementation of Single Scale Retinex (SSR) Image Enhancement Algorithm

Single Scale Retinex (SSR) is a classical image enhancement algorithm primarily used to improve images with uneven illumination. By simulating the human visual system's adaptability to lighting variations, this algorithm effectively enhances the visual quality of images. The core implementation approach of SSR in MATLAB involves the following computational steps: Image Input Processing: The target image is first read using imread() function and converted to double-precision floating-point format using im2double() to facilitate subsequent calculations. For color images, the algorithm typically processes the R, G, and B channels separately using channel-wise operations. Gaussian Filtering: Gaussian blur is applied to the input image using fspecial('gaussian', hsize, sigma) to create a Gaussian kernel, which simulates the smooth illumination component. The kernel size (hsize) and standard deviation (sigma) are critical parameters that directly influence the final enhancement results. A larger sigma preserves more global illumination information, while a smaller sigma enhances local contrast through more aggressive filtering. Logarithmic Domain Computation: Both the original image and Gaussian-blurred image undergo logarithmic transformation using log() function. The difference between these two log-transformed images is then calculated. This step is based on Retinex theory, aiming to separate the reflection component (image details) from the illumination component through logarithmic operations. Dynamic Range Adjustment: Since logarithmic calculations may result in abnormal output value ranges, linear stretching or normalization is performed using mat2gray() or imadjust() functions to map pixel values to a reasonable display range (typically 0-255). This step ensures proper visualization of the enhanced image. Result Output: The final enhanced image demonstrates clearer details in shadow regions while preventing over-exposure in highlight areas. The output can be displayed using imshow() or saved with imwrite() function. Although SSR has simple computational requirements, it significantly improves static images with uneven illumination (such as backlit faces or medical images). In practical applications, it can be further optimized by combining with Multi-Scale Retinex (MSR) or color restoration algorithms for enhanced performance.