The obtained images are frequently flawed because of a variety of environmen- tal issues, particularly at night, such as inside illumination, cloudy weather, etc. The dark image has a compressed dynamic range that can be improved in order to see the finer details. This research effort proposes an improved lighting reflection model-based technique for improving extremely dim images. This improved method relies on reconstruction carried out via morphological processing with Top-hat transformation and Contrast Limited Adaptive His- togram Equalization (CLAHE). The HSV colour scheme is used to perceive the image, and the V component is estimated. The inverse of the intensity component (V) is calculated after normalising the intensity component. The negative image is then subjected to the CLAHE algorithm. The final step is to apply multiscale image enhancement to the obtained image. The brightness component of an image is adaptively adjusted via gamma enhancement. The outcomes of two gamma-enhanced photographs with various gamma values are produced after gamma enhancement. The significant information that can be used for image fusion is extracted from these images using principal com- ponent analysis. The weight value is adaptively determined during the PCA- based image fusion employing morphological Top-hat modification to enhance the image quality and highlight the erratic background pixels. The suggested technique emphasises edge and structural preservation while enhancing detail in extremely dim photos. Results from experimental validations demonstrate that the suggested strategy outperforms the current method in terms of both qualitative and quantitative measures.