To provide optimal image quality, digital filters should account for both the count level and the object imaged. That is, they should be image-dependent. By using the constraint equation of constrained least-squares (CLS) restoration to determine one parameter of the Metz filter, a filter which adapts to the image has been developed. This filter has been named the Constrained Least-Squares Metz filter. The filter makes use of a regression relation to convert the Metz filter parameter determined using the CLS criterion to the value which would minimize the normalized mean square error (NMSE). The regression relation and the parameters which specify the general form of the Metz filter were determined using images of the Alderson liver and spleen phantoms. The designed filter was tested for its ability to adapt to other objects with images from each of three different test objects. When the values of the Metz filter parameters for these images determined by the CLS-Metz filter were compared by a regression analysis to those which minimized the NMSE for each image, a correlation coefficient of 0.98, a slope of 0.95, and a zero intercept of 0.1 were obtained. With clinical images, the CLS-Metz filter has been shown to provide consistently good image quality with images as diverse as heart perfusion images and bone studies.