A Retinex-Based Variational Model for Enhancement and Restoration of Low-Contrast Remote-Sensed Images Corrupted by Shot Noise

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing 2020, Vol.13, p.941-949
• Main Authors: Febin, I. P., Jidesh, P., Bini, A. A.
• Format: Article
• Language: English
• Subjects: Adaptation models; Atmospheric conditions; Atmospheric modeling; Bayesian analysis; Comparative analysis; Comparative studies; Contrast enhancement; Defects; denoising; Earth; Image contrast; Image enhancement; Image restoration; Imaging techniques; Inhomogeneity; Lighting; Noise; perceptual image processing; Poisson distribution; Probability theory; Remote sensing; Restoration; Retinex (algorithm); Shot noise; Statistical analysis; variational method
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2020.2975044

Remotely sensed images are widely used in many imaging applications. Images captured under adverse atmospheric conditions lead to degraded images that are contrast deficient and noisy. This study is intended to address these defects of remotely sensed data efficiently. A perceptually inspired variational model is designed based upon the Bayesian framework, powered by the retinex theory. The atmospheric noise or the shot noise (precisely following a Poisson distribution) and contrast inhomogeneity are addressed in this article. The model thus designed is tested and verified both visually and quantitatively using various test data under different statistical measures. The comparative study reveals the efficiency of the model.