Contrast enhancement is a crucial preprocessing method for enhancing the efficiency of subsequent image processing and computer vision tasks. In the past, a lot of effort has been put into improving the visual scenes of pictures taken in low light. Images taken in poor illumination environments frequently reveal issues like color distortion, noise, low brightness, etc., that negatively impact the visual influence on human eyes. Therefore, an approach for improving poorly illuminated images based on wavelet transform is suggested to get around this problem. The input image is first transformed to Kekre's LUV color space, after which discrete wavelet transform (DWT) is applied to part eachchannel into low and high-frequency components. As the illumination is concentrated on the low-frequency image component, the Exposure-based Sub Image Histogram Equalization (ESIHE) technique is applied to enhance the im-age's lighting. Besides, limited adaptive histogram equalization(CLAHE) is imposed to control the over-enhancement of specific region's contrast. Modified L, U, and V components are recovered via the inverse discrete wavelet trans-form (IDWT), and the image is again converted into RGB space. This output is fused with a histogram equalized image using weighted fusion followed by a high boost filter to get the final enhanced output. Experimental outcomes are achieved to validate the efficacy and robustness of the suggested strategy using quality evaluators such as Entropy, NIQE, and BRISQUE rankings explored on ExDark, DPED, and LoLi datasets