Recently, the authors proposed a Look Up Table (LUT) based method for inverse halftoning of images. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. Many of the entries in the LUT are unused because the corresponding binary patterns hardly occur in commonly encountered halftones. These are called nonexistent patterns. In this paper, we propose a tree structure which will reduce the storage requirements of an LUT by avoiding nonexistent patterns. We will demonstrate the performance on error diffused images and ordered dither images. Then, we introduce LUT based halftoning and tree-structured LUT (TLUT) halftoning. Even though TLUT method is more complex than LUT halftoning, it produces better halftones and requires much less storage than LUT halftoning.We will demonstrate how error diffusion characteristics can be achieved with this method. Afterwards, our algorithm will bet rained on halftones obtained by Direct Binary Search (DBS).The complexity of TLUT halftoning is higher than error diffusion algorithm but much lower than DBS algorithm. Also, the halftone quality of TLUT halftoning increases if the size of TLUT gets bigger. Thus, halftone image quality between error diffusion and DBS will be achieved depending on the size of tree-structure in TLUT algorithm.

We propose a method to modify the histogram of a signal to a desired specific histogram. Traditionally, points having the same value in the input signal are all mapped to same value in the output signal. Hence, the desired histogram can only be approximated. Here we formulate our problem as finding a transformation such that the error between the input and output signal is minimized and the output signal has the desired histogram. It turns out that this problem is equivalent to an integer linear programming problem. This method might be specifically useful for histogram-based watermarking and compression.

In this paper we propose look-up table (LUT) based methods for inverse halftoning of images. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. The method is extremely fast (no filtering is required) and the PSNR and visual image quality achieved is comparable to the best methods known for inverse halftoning. The LUT inverse halftoning method does not depend on the specific properties of the halftoning method, and can be applied to any halftoning method. Then, an algorithm for template selection for LUT inverse halftoning is introduced. We demonstrate the performance of the LUT inverse halftoning algorithm on error diffused images and ordered dithered images. We also extend LUT inverse halftoning to color halftones.

In a previous paper we have proposed a method for inverse halftoning of monochrome images. It is a look up table (LUT) based method, obtained from the histogram gathered from a few sample halftone images and corresponding original images. The method is extremely fast (no filtering is required) and the image quality achieved is comparable to the best methods known for inverse halftoning. The method does not depend on the specific properties of halftoning method, and can be applied to any halftoning method. In this paper, we discuss how to choose the template recursively for the LUT. Afterwards, we propose an LUT based inverse halftoning method for color halftones. Color inverse halftoning can be done independently for each color plane or the correlation between the planes can be exploited. We also show how to choose the template for color inverse halftoning.

We have previously applied the look up table (LUT) method for inverse halftoning. We also proposed a tree-structure LUT inverse halftoning in order to reduce the memory requirements of the LUT method. We introduce the LUT based halftoning method. Pixels from a causal neighborhood and the contone value of the current pixel are included in the LUT. The LUT halftoning requires no arithmetic operations other than memory access. For any halftoning method, a sample set of images and halftones of these images is used. We introduce the tree-structure LUT (TLUT) halftoning. Even though this method is more complex than LUT halftoning it produces better halftones and it requires much less storage than LUT halftoning. We demonstrate how the error diffusion characteristics can be achieved with this method. The algorithm is trained on halftones obtained by direct binary search (DBS) images. The complexity of the tree-structure LUT halftoning is higher than the error diffusion algorithm but much lower than the DBS algorithm. Also, the halftone quality of TLUT halftoning increases if the size of TLUT gets larger. Thus, the halftone image quality between the error diffusion and DBS is achieved depending on the size of the tree-structure in the TLUT algorithm.

In this paper we propose a novel method for inverse halftoning of images. The method is basically a Look Up Table (LUT) based method. The LUT for inverse halftoning is obtained from the histogram gathered from a few sample halftone images and corresponding original images. The method is extremely fast (no filtering is required) and also the image quality achieved is comparable to the best methods known for inverse halftoning. The method does not depend on the specific properties of halftoning method, and can be applied to any halftoning method.

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.

The dot diffusion method for digital halftoning has the advantage of parallelism unlike the error diffusion method. The method was previously improved by optimization of the so-called class matrix so that the resulting halftones are comparable to the error diffused halftones. We give a mathematical description of the dot diffusion method. This description is then applied in inverse halftoning.

The dot diffusion method for digital halftoning has the advantage of parallelism unlike the error diffusion method. However, the image quality offered by error diffusion is still regarded as superior to other known methods. In a previous paper we showed how the dot diffusion method can be improved by optimization of the so-called class matrix. In this paper we first review the dot diffusion algorithm and the optimization of the class matrix. A method for inverse halftoning of dot diffused images is then proposed. The method uses wavelet decomposition to eliminate the halftoning noise and does not make use of the knowledge of the class matrix.