Images are vital in various fields, but digital images create substantial data volumes, posing storage and transmission challenges. Image compression addresses these issues by reducing data sizes. In this study, Contrast Limited Adaptive Histogram Equalization (CLAHE) is used for enhancement of images. Further, this study focuses on the use of Discrete Wavelet Transform (DWT) for image compression and Inverse Discrete Wavelet Transform (IDWT) based on thresholding for reconstruction of images. Five different wavelets which are haar, db2, sym3, bior1.3, coif3 are used for compression of images. After that different thresholding values are taken for coif3 for different images for reconstruction of images. Different performance metrics like Peak Signal-to-Noise Ratio (PSNR) and Mean Squared Error (MSE) are computed to check the effectiveness of the system. The Coif3 wavelet achieved a PSNR of 33.87 dB for the Lena image, 29.76 dB for flower image, 21.03 dB for house image and 33.93 for peppers image which outperforms for all other wavelets at threshold value of 10. In reconstruction of images using IDWT, proposed system is achieving maximum PSNR at minimum threshold value. These findings provide insights into the effectiveness of wavelet-based compression and reconstruction techniques, guiding the development of efficient algorithms for diverse applications.