Efficient fax transmission of halftone images

Reproduction couleur par trames irrégulières et semi-régulières

Reading performance and visual comfort with scale to grey compared with black-and-white scanned print

This paper presents a system for reducing moire artifacts in halftoned images. Moire artifacts arise when the source image contains periodic texture with a frequency close to that of the halftone screen. In addition, moire-like artifacts occur with aperiodic texture in the frequency range of the halftone screen, as well as fine lines. The system is effective in suppressing each of these classes of artifact. The system first generates a trial halftone using a standard halftone screen. It then analyzes the moire present in this trial halftone, generating a correction signal, which is then added to the source image to form a compensated image. This compensated image is then halftoned using a second halftone screen to produce the final resulting halftone. Several refinements are discussed which reduce potential artifacts caused by the system.

Digital halftone is the technique used to convert gray-scale images into binary ones, simulating gray shades by scattering appropriately black and white pixels. Sometimes, there arises the necessity of increasing the resolution of a halftone image. Some recent works have proposed a number of learning-based techniques to zoom binary images. However, they cannot consider a large neighborhood to decide the colors of the resolution-increased pixels, because their running time skyrockets with the growth of the window and the sample images sizes. The use of large window and samples is required to zoom halftone images accurately. This paper presents a new technique to zoom quickly and precisely images generated by any locally-decided halftone algorithm. It is based on decision-tree learning and it is very fast, even using a large window or large samples. The zoomed images obtained by this technique are incredibly sharp and accurate.

The grapevine leaves was analyzed based on fractal geometry, and the study evaluated interdependencies between parameters specific to fractal geometry. The grape varieties considered in the study were represented by Chasselas (Ch), Cabernet Sauvignon (CS), Muscat Hamburg (MH), Muscat Iantarnîi (MI), Muscat Ottonel (MO), Perlă de Csaba (PC), and Victoria (Vi). Fresh leaves were taken from each variety. The leaves were herborized to preserve the integrity of the leaves, and were scanned. Imaging analysis and fractal analysis were used to generate fractal geometry parameters: foreground pixels (FP), fractal dimensions D1 (MEE = 0.5), D2 (MEE = 0.2), BW parameter (black and white pixels), B+BW parameter (black + black and white pixels), W+BW parameter (white + black and white pixels). The descriptive statistical analysis generated the analysis report based on a number of 15 samples for each grapevine variety. Correlation analysis was used to evaluate the interdependence between fractal geometry parameters, for each grape variety. Correlations with different levels of intensity were recorded between the parameters of the fractal geometry, in relation to the vine variety. The values ​​of the correlation coefficient showed some matches and differences of the parameters of the fractal geometry in the seven grape varieties studied. In all cases, a very strong positive correlation was recorded between D1 and D2. Also, a negative correlation was recorded between W+BW and B+BW parameters, but of variable intensity.

Document image segmentation and quality improvement by moiré pattern analysis

Data hiding and reversible data hiding research has primarily focused on grayscale and color images, because binary and halftone images are prone to visual distortion caused by a small number of errors in pixel representation. As a result, reversible data hiding is more useful than halftone-based data hiding. This study proposes an investigation of encrypted halftone images based on dual reversible data hiding, which improves the reversibility and security of the image by utilizing a dual cover image. Since halftone images are adequately compressed, they are beneficial in low-channel-bandwidth environments. Hamming code (HC) (7,4) is applied to each block of the halftone image to hide the secret data, and two halftone images are recorded and sent to different receivers at the end of the embedding process. Recipients can use the proposed method and the two marked images to extract the message and recover the cover halftone image. The proposed data hiding method can enhance the quality of the decrypted image by appropriately increasing the block size, and conversely, sufficiently large amounts of data can be hidden by reducing the block size. The experimental results provide evidence of the effectiveness of the proposed method in terms of both image quality and the embedding rate.

Face recognition is done gray or color images. Face recognition can be also done in binary images. Halftone images are binary images having gray shade information. These images are more informative as compared to binary images. Halftone images can be generated from different kernels. The quality of a halftone image depends on the kernel used to generate it. Halftone images can be used to generate human recognizable features for face recognition. The human recognizable features are fed to multiclass SVM classifiers for face recognition. The performance of using different human recognizable features generated from halftone images of different standard kernels is studied. It is found that some of kernels give higher recognition rate than the other. Also, the performance is also dependent on size of the window used for feature extraction. In most cases, the minimum size window 3 × 3 gives higher recognition rate in most of the cases. Of the standard kernels Frankie Siera-3, Stucki kernels give higher recognition rate and of the proposed kernels P2 kernel gives higher recognition rate than other proposed kernels.

An original local-potential morphometric algorithm for information-entropy estimation of the impact of technogenic chemical pollution on forests was proposed. This algorithm is distinguished by a procedure of separation of binary objects—pixel clusters (simply connected, tightly packed sets of white and black pixels)—in satellite photographs by a potential transformation and a procedure of estimation of the changes in the local configuration of pixel clusters by calculating the information entropy for the virtual potential of models of the interaction of pseudoparticles (white and black pixels) of the binary image. This enables one to practically estimate the spatial disturbances in forests by the impact of technogenic chemical pollution using point statistical estimates of the geometric indices of clusters. The proposed algorithm differs from conventional ones that determine the boundaries of binary objects by statistical analysis using co-occurrence matrices or point statistical estimates of the configuration of pixels.

In this paper, we present an image authentication scheme for halftone images based on the error diffusion technique. Our scheme converts a gray-level image into a halftone image by using the error diffusion technique and embeds the authentication codes in the obtained halftone image at the same time. From the experimental results, our proposed scheme can detect the tampered places according to the embedded authentication codes. Besides, the visual quality of the stego halftone image in our scheme is satisfactory.

This paper presents an improved XOR-based Data Hiding Scheme (XDHS) to hide a halftone image in more than two halftone stego images. The hamming weight and hamming distance is a very important parameter affecting the quality of a halftone image. For this reason, we proposed a method that involves minimizing the hamming weights and hamming distances between the stego image and cover image in 2×2-pixel grids. Moreover, our XDHS adopts a block-wise operation to improve the quality of a halftone image and stego images. Furthermore, our scheme improves security by using a block-wise operation with A-patterns and B-patterns. Our XDHS method achieves a high quality with good security compared to the prior arts. An experiment verified the superiority of our XDHS compared with previous methods.

ABSTRACTInverse synthetic aperture radar (ISAR) imaging is an efficient technique to generate two‐dimensional spatial distributions of radar cross‐section of a target. The received signal of ISAR contains interphase errors resulting in blurring problem in the images due to motion effects of the target. In this study, a simple, stable, and robust method based on artificial bee colony (ABC) algorithm has been proposed for motion compensation (MoComp) to overcome the blurring problem in ISAR images. The minimum entropy was used as objective function in the ABC algorithm for optimally determining the parameters of velocity and acceleration. The proposed method was implemented to four different scenarios based on the two air target models to show performance of the ABC algorithm. The radar signal of the target without MoComp was given as inputs to the ABC algorithm to determine velocity and acceleration values so as to provide the clearest ISAR image. The proposed method was compared with a suggestion reported elsewhere using particle swarm optimization (PSO), PSO with island model, genetic algorithm. It is pointed out that the proposed method is more successful, stable and efficient in terms of the image quality, entropy values, and standard deviation. © 2014 Wiley Periodicals, Inc. Microwave Opt Technol Lett 56:2691–2698, 2014

One of the universally studied research topics is a reversible data hiding approach because of its wide applications in medical image transmission, cloud computing as well as in military services. In the introduced scheme stated in this paper, a new RDH scheme in encrypted images is proposed which ensures a good embedding rate without compromising the bit error rate. The scheme introduced in this manuscript uses a new measure that is computationally efficient and ensures real-time performance. In the proposed scheme, the original image is divided into blocks and in each block, one secret message bit is hidden by the data hider. All the pixels in the image block are categorized into two classes: black pixels and white pixels based on the predefined checkerboard pattern. The data hiding is performed by mapping the pixel values in the white position to a new value, only if the data hider wants to hide a secret bit 1. At the time of data extraction and image recovery, the closeness between the adjacent pixels is analyzed by considering a pair of adjacent black pixels and white pixels from the quantized image blocks. The overall experimental study of the proposed scheme is carried out using the standard images from the USC-SIPI image dataset managed by the University of Southern California.

The limited bandwidth of underwater acoustic channels poses a challenge to the efficiency of multimedia information transmission. To improve efficiency, the system aims to transmit less data while maintaining image utility at the receiving end. Although assessing utility within compressed information is essential, the current methods exhibit limitations in addressing utility-driven quality assessment. Therefore, this paper introduces a Distillation-based Compacted Information Quality assessment metric (DCIQ) for utility-oriented quality evaluation in the context of underwater machine vision. This method is conducted in the Utility-oriented compacted Image Quality Dataset (UIQD) that contains utility qualities of reference images and their corresponding compressed information at different levels. The utility score is derived from the average confidence of various object detection models. In DCIQ, utility features of compacted information are acquired through transfer learning and mapped using a Transformer. Besides, we propose a utility-oriented cross-model feature fusion mechanism to address different detection algorithm preferences. After that, a utility-oriented feature quality measure assesses compacted feature utility. Finally, we utilize distillation to compress the model by reducing its parameters by 55%. Experiment results effectively demonstrate that our proposed DCIQ can predict utility-oriented quality within compressed underwater information

In this paper, a low computational complexity method is proposed to embed a multi-tone visual pattern using the concept of secret sharing. One halftone image is obtained using regular error diffusion, and the other image is obtained using the proposed Generalized Noise-Balanced Error Diffusion (GNBEDF) in considering the properties of the watermark and the first halftone image in the same time. With the proposed method, the bi-level watermark embedding becomes a special case to this technology. The bi-level watermark decoding can be simply achieved via printing the two halftone images onto two transparencies and then superimposing them together to reveal the watermark pattern. However, as the multi-tone watermark is involved, the proposed Gaussian Difference (GD) approach is needed to perform the decoding to produce a decoded multi-tone watermark. Throughout this work, a lowpass filter obtained by Least-Mean-Square is employed to assess the image quality.