Image processing has a major impact on image quality and diagnostic performance of digital chest radiographs. Goals of processing are to reduce the dynamic range of the image data to capture the full range of attenuation differences between lungs and mediastinum, to improve the modulation transfer function to optimize spatial resolution, to enhance structural contrast, and to suppress image noise. Image processing comprises look-up table operations and spatial filtering. Look-up table operations allow for automated signal normalization and arbitrary choice of image gradation. The most simple and still widely applied spatial filtering algorithms are based on unsharp masking. Various modifications were introduced for dynamic range reduction and MTF restoration. More elaborate and more effective are multi-scale frequency processing algorithms. They are based on the subdivision of an image in multiple frequency bands according to its structural composition. This allows for a wide range of image manipulations including a size-independent enhancement of low-contrast structures. Principles of the various algorithms will be explained and their impact on image appearance will be illustrated by clinical examples. Optimum and sub-optimum parameter settings are discussed and pitfalls will be explained.