A class of inverse halftoning algorithms that recovers grayscale (continuous-tone) images from halftone images is proposed. The basic structure is an optimized linear filter. Then, a properly designed adaptive postprocessor is employed to enhance the recovered image quality. Finally, a multistage space-varying algorithm is developed that uses the basic linear filter structure as before but with spatially adaptive parameters.

A class of inverse halftoning methods together with post-processing is proposed in this paper. In order to optimize the inverse operation of halftoning, we adopt the inverse modeling concept to find the least-squares solution of this problem. And then, the knowledge of image characteristics helps us in designing improved algorithms that produce the most promising pictures to our eyes. They are conceptually rather different from the traditional inverse halftoning methods that use simple low-pass filters. Furthermore, we develop a general space-varying sliding-window filter (SV-SWF) scheme. The goal is to be able to handle various types of pictures using the same basic inverse halftoning structure together with spatially adaptive parameters. This universal inverse halftoning operator is achieved by using an adequate classification scheme to separate image data into different groups, each corresponding to a set of pre-trained parameters. Due to the additional information in the inputs and the outputs utilized in our processing, better reconstructed images are obtained.>