Dynamical stochastic resonance for non‐uniform illumination image enhancement

Images taken under poor illumination conditions have low contrast and dark tones. General dark image enhancement algorithms cannot effectively enhance these images without introducing over‐enhancement, detail loss, and noise amplification. In this study, a simple and fast enhancement technique of non‐uniform illumination images is proposed based on dynamical stochastic resonance (DSR). The low‐contrast images are enhanced through the nonlinear iteration by solving monostable Langevin equation. Iteration parameters are dynamically adjusted according to the intensity distribution of the original images, which ensure the balance of visibility and naturalness in the entire areas. A threshold is defined to automatically confirm the optimal outputs. The enhanced image is obtained by fusing the DSR result, original component, and illumination compensation component. The computational time, no‐reference perceptual quality assessment, and lightness order error are measured to evaluate the performance of experimental results. The subjective and objective comparison with state‐of‐the‐art methods shows that our method performs well to enhance the non‐uniform illumination images with a low‐computational complexity.