Medical diagnosis is a multifaceted process that includes images from angiography, ultrasound imaging, x-rays, and other sources. Provide visual distortion and distractions. In this work, the problem of removing rough Gaussian additive white noise from computed tomography (CT) pictures without sacrificing fine features is tackled. The difficulty kasiof producing a clear image from a noisy and fuzzy image-which can be accomplished with the perfect pinhole camera-without knowing how hazy the image is known as blind image deconvolution. MATLAB-based medical image processing has several advantages for the medical field. It improves diagnosis by removing critical information from medical pictures such as MRIs, CT scans, and X-rays using sophisticated algorithms. The proposed approach is synthesized by using the concept of wavelet based method of de-noise using MATLAB. The input signal is first decomposed into various wavelet coefficient using discrete wavelet transform. These wavelet coefficients are threshold and are collected together by using inverse discrete twilight transform and we get de-noised image back.