The principal source of blur in digital images arise during image acquisition (digitization) or transmission. The performance of imaging sensors is affected by a variety of factors, such as the environmental conditions during image acquisition. Blurry images are the result of movement of the camera during shooting (not holding it still) or the camera not being capable of choosing a fast enough shutter speed to freeze the action under the light conditions. For instance, in acquiring images with a camera, light levels and sensor temperature are major factors affecting the amount of blur in the resulting image. Blur was implemented by first creating a PSF filter in MatLab that would approximate linear motion blur. This PSF was then convolved with the original image to produce the blurred image. Convolution is a mathematical process by which a signal, in this case the image, is acted on by a system, the filter, in order to find the resulting signal. The amount of blur added to the original image depended on two parameters of the PSF: length of blur (in pixels), and the angle of the blur. This thesis work is going to provide a new, faster, and more efficient noise reduction method for images corrupted with motion blur. This new filter has two separated steps or phases: the detection phase and the filtering phase. The detection phase uses fuzzy rules to determine whether a image is blurred or not. When blurry image is detected, Then we use fuzzy filtering technique focuses only on the on the real blurred pixels.