Models that researchers often use for the dehazing task are based on the Koschmieder law. In this article, we use the STRESS (Spatio-Temporal Retinex-inspired Envelope with Stochastic Sampling) model for the dehazing task. In our work, we demonstrate theoretically and empirically how the parameters in the STRESS framework can be set for dehazing. We then propose a new algorithm for haze removal, based on the model of the (STRESS) framework, which combines edge detection and Hidden Markov Model (HMM) to solve the problem. Experiments show that our approach yields more visibility—based on some metrics and psychophysical tests—than most of the state-of-the-art approaches.