Enhancing low-light images is used for various applications such as photo surveillance, intelligent driving, and military scenarios. Improving visibility ensures that critical information is not lost in poorly lit environments. Low- light images suffer from reduced contrast, increased noise, poor visibility and hazy visibility. The proposed method will be utilized for colour constancy and dynamic range enhancement, effectively improving the perceived brightness and colour in low-light images. The objectives of this project include improving the visual quality of low light images by enhancing brightness, contrast, and overall clarity and to ensure that critical details are visible even in challenging lighting conditions. This method uses illumination-reflection based Retinex model and bilateral filter for low light image enhancement. Retinex theory proposes a computational model for colour vision and perception in the human visual system. The bilateral filter is a non-linear image filtering technique that combines spatial and intensity information to smooth images while preserving edges and details. The bilateral filter also considers the similarity in intensity or colour between neighbouring pixels. Despite higher-than-expected Lightness Order Error (LOE) values, image quality metrics like visual information fidelity (VIF), Naturalness Image Quality Evaluator (NIQE) and Blind reference image spatial quality evaluator (BRISQUE) show improvements, indicating better quality. The proposed method shows promising results for darker images than the algorithm using gaussian filter.