OBJECTIVES: The authors introduce a Bayesian algorithm for digital chest radiography that increases the signal-to-noise ratio, and thus detectability, for low-contrast objects. METHOD: The improved images are formed as a maximum a posteriori probability estimation of a scatter-reduced (contrast-enhanced) image with decreased noise. Noise is constrained by including prior knowledge of image smoothness. Variations between neighboring pixels are penalized for small variations (to suppress Poisson noise), but not for larger variations (to avoid affecting anatomical structure). The technique was optimized to reduce residual scatter in digital radiographs of an anatomical chest phantom. RESULTS: The contrast in the lung was improved by a factor of two, whereas signal-to-noise ratio was improved by a factor of 1.8. Image resolution was unaffected for objects with a contrast greater than 2%. CONCLUSION: This statistical estimation technique shows promise for improving object detectability in radiographs by simultaneously increasing contrast, while constraining noise.