Retinexbaserade

Retinex-based refers to a class of image processing and computer vision techniques inspired by the Retinex theory, a model of human vision proposed by Edwin Land in the 1950s and 1960s. The theory suggests that the human visual system perceives color and brightness through a combination of retinal processing and cortical computation, where the retina captures raw light data and the brain refines it to produce stable color perception under varying lighting conditions.

Retinex-based methods aim to replicate this process computationally, particularly in tasks such as color constancy, high dynamic range imaging (HDR), and image enhancement. These algorithms typically involve decomposing an image into reflectance and illumination components. The reflectance component represents the intrinsic color and texture of surfaces, while the illumination component accounts for lighting variations, such as shadows and highlights. By separating these components, Retinex-based techniques can improve color accuracy, contrast, and overall visual quality in images captured under complex lighting conditions.

One of the most well-known Retinex-based algorithms is the Retinex algorithm developed by Jobson, Murray, and Healey in 1997. This algorithm uses a multi-scale Gaussian filter to approximate the retinal processing stage, followed by a logarithmic transformation to model cortical refinement. Variations of this approach have been applied to enhance low-light images, reduce noise, and improve dynamic range in photography and computer vision applications.

Retinex-based methods are particularly useful in scenarios where lighting conditions are unpredictable, such as in outdoor photography or medical imaging. However, they can also introduce artifacts, such as color shifts or unnatural enhancements, if not carefully calibrated. Modern implementations often incorporate machine learning to refine traditional Retinex techniques, balancing computational efficiency with visual fidelity.