Images captured in adverse weather conditions, such as haze, fog, smog, or mist, have reduced visibility, contrast, and color fidelity. These impairments challenge various computer vision applications, such as intelligent transportation, video surveillance, weather forecasting, and remote sensing. While many daytime dehazing techniques exist, they are less effective for nighttime images, which have additional issues, such as nonuniform illumination, texture blurring, glow effects, color distortion, noise, and low light. This paper proposes a novel method for improving the quality of nighttime images affected by haze and low-light conditions. Our method, Nighttime Dehazing, Low-Light Enhancement, and Light Suppression (NDELS), integrates three key processes: enhancing visibility, brightening low-light areas, and suppressing glare from bright light sources. We also introduce a novel method for generating training data to help our model learn light suppression better. We evaluate our method against eight state-of-the-art algorithms on four diverse datasets. The simulation results show that the presented method outperforms the state-of-the-art methods quantitatively and qualitatively. For example, our method (i) improves the overall image quality, color fidelity, and edges and (ii) achieves 8.8% higher PSNR, and 4.5% higher SSIM scores, and a better subjective rating. Moreover, our method enhances real-world object detection tasks, surpassing other methods in performance.