Optimized halftoning using dot diffusion and methods for inverse halftoning

Unlike the error diffusion method, the dot diffusion method for digital halftoning has the advantage of pixel-level parallelism. However, the image quality offered by error diffusion is still regarded as superior to most of the other known methods. We show how the dot diffusion method can be improved by optimization of the so-called class matrix. By taking the human visual characteristics into account we show that such optimization consistently results in images comparable to error diffusion, without sacrificing the pixel-level parallelism. Adaptive dot diffusion is also introduced and then a mathematical description of dot diffusion is derived. Furthermore, inverse halftoning of dot diffused images is discussed and two methods are proposed. The first one uses projection onto convex sets (POCS) and the second one uses wavelets. Of these methods, the wavelet method does not make use of the knowledge of the class matrix. Embedded multiresolution dot diffusion is also discussed, which is useful for rendering at different resolutions and transmitting images progressively.