High-quality Color Images Research Articles

Taxonomy of the Cryptocephalus heraldicus Group (Coleoptera: Chrysomelidae, Cryptocephalinae) from China

This is a study on the leaf beetle subgenus Cryptocephalus Geoffroy, 1762 from China, with the particular emphasis upon the species-group classification of the subgenus and the taxonomy of the Cryptocephalus heraldicus species group. A new key is compiled to all the species groups found in China. Four new species are described from China: Cryptocephalus (Cryptocephalus) biordopunctatus sp. nov. from Yunnan, C. hani sp. nov. from Shanxi, Hubei, Shaanxi and Gansu, C. incisodentatus sp. nov. from Sichuan and Yunnan, and C. nigroflavusiventerus sp. nov. from Yunnan. Three species are found for the first time in China: C. lacosus Pic, 1922, C. nigriceps Allard, 1891 and C. rajah Jacoby, 1908. The species C. nigrolimbatus Jacoby, 1890 is transferred from the subgenus Burlinius Lopatin to this subgenus and assigned to the Cryptocephalus heraldicus group. The species number of this group is now 30 in total according to our result of taxonomic review. A key to all the mainland China species of this species group is provided as well as high quality color images and line drawings of adult habitus, aedeagus, and other important structures. All the types of the new species are deposited in the collection of Institute of Zoology, Chinese Academy of Sciences (IZ-CAS).

Two new species of the genus Melixanthus Suffrian (Coleoptera, Chrysomelidae, Cryptocephalinae) from China.

Two new species of the genus Melixanthus Suffrian, 1854 are described from China: M.menglaensis Duan, Wang & Zhou, sp. nov. from Yunnan (also in Vietnam, Tonkin) and M.similibimaculicollis Duan, Wang & Zhou, sp. nov. from Yunnan. Another species, M.rufiventris Pic, 1926, is reported for the first time in China. High-quality color images and line drawings of adult habitus, aedeagus, and other important structures are provided for all three species. The types of the new species are deposited in the collection of Institute of Zoology, Chinese Academy of Sciences (IZ-CAS).

A WiFi scanner in conjunction with disposable multiplex paper assay for the quantitation of disease markers in blood plasma.

Herein we report a quantitative, multiplex assay for disease markers in plasma based on an integrated setup of a portable scanner and a disposable paper-based analytical device (PAD). The quantitative analysis relies on the digital colorimetric reading of the three-layer PAD with 30 assay sites for performing respective chromogenic reactions for plasma uric acid, glucose, and triglyceride, which are considered as important risk factors for cardiovascular diseases. A portable scanner with WiFi transmission capability was used to produce high-quality color images of the PADs and wirelessly transfer them to a smartphone or other mobile devices for data processing. The concentrations of biomarkers in both standard solutions and plasma samples can be directly obtained using acustom-designed smartphone app that is also capable of constructing calibration curves. The detection limits of uric acid, glucose, and triglyceride were determined to be 0.50mg/dL, 0.84mmol/L, and 14mg/dL, respectively, which are below the normal limits and adequate for clinical validation. Owing to the distinct advantages-simple, portable, and cost-effective-this mobile assay protocol can be used for point-of-care (POC) settings or resource-limited situations, and potentiallyfor the diagnosis and prevention of infectious diseases.

A Colorization Framework for Monochrome-Color Dual-Lens Systems Using a Deep Convolutional Network

In monochrome-color dual-lens systems, the monochrome camera can capture images with higher quality than the color camera. To obtain high quality color images, a better approach is to colorize the gray images from the monochrome camera with the color images from the color camera serving as a reference. In addition, the colorization may fail in some cases, which makes the estimation of the colorization quality a necessary step before outputting the colorization result. To solve these problems, we propose a deep convolutional network based framework. 1) In the colorization module, the proposed colorization CNN uses deep feature representations, attention operation, 3-D regulation and color correction to make use of colors of multiple pixels in the reference image for colorizing each pixel in the input gray image. 2) In the colorization quality estimation module, based on the symmetry property of colorization, we propose to utilize the colorization CNN again to colorize the gray map of the original reference color image using the first-time colorization result from the colorization module as reference. Then, the quality loss of the second-time colorization result can be used for estimating the colorization quality. Experimental results show that our method can largely outperform the state-of-the-art colorization methods and estimate the colorization quality accurately as well.

First description of the male Quelaestrygon puetzi Smetana, 1999 (Coleoptera, Staphylinidae, Staphylinini) from China.

A male of the very rare and phylogenetically puzzling species, Quelaestrygon puetzi Smetana, 1999, is described for the first time based on a single specimen from Sichuan Province, China. High quality color images and line drawings of the male external and genitalic traits are provided.

Image Demosaicing Based on Generative Adversarial Network

Digital cameras with a single sensor use a color filter array (CFA) that captures only one color component in each pixel. Therefore, noise and artifacts will be generated when reconstructing the color image, which reduces the resolution of the image. In this paper, we proposed an image demosaicing method based on generative adversarial network (GAN) to obtain high-quality color images. The proposed network does not need any initial interpolation process in the data preparation phase, which can greatly reduce the computational complexity. The generator of the GAN is designed using the U-net to directly generate the demosaicing images. The dense residual network is used for the discriminator to improve the discriminant ability of the network. We compared the proposed method with several interpolation-based algorithms and the DnCNN. Results from the comparative experiments proved that the proposed method can more effectively eliminate the image artifacts and can better recover the color image.

FPGA Acceleration of Color Interpolation Algorithm Based on Gradient and Color Difference

To quickly and efficiently achieve a high-quality color image on the embedded platform, designed and used FPGA to complete Bayer pattern image Color interpolation. In this paper, we proposed a color interpolation algorithm based on image gradient direction and color difference. The algorithm calculates the G component according to the gradient value and color difference at the pixel to be interpolated, and then calculates the missing R component and B component based on the G component interpolation. Finally, the pixels of the image boundary area are interpolated. Tested on the Kodak dataset, the experimental results show that the subjective visual quality and objective image quality evaluation of the algorithm is superior to the conventional algorithm. On the other hand, and reduce problems such as image aliasing and edge blurring. The proposed algorithm has been applied to the actual Xilinx’s FPGA platform, a real-time speed of 120 MHz, enabling real-time image color Interpolation.

Adversarial Training Methods for Boltzmann Machines

A Restricted Boltzmann Machines (RBM) is a generative Neural Net that is typically trained to minimize KL divergence between data distribution P data and its model distribution P RBM . However, minimizing this KL divergence does not sufficiently penalize an RBM that place a high probability in regions where the data distribution has a low density, and therefore, RBMs always generate blurry images. In order to solve this problem, this paper extends the loss function of RBMs from KL divergence to adversarial loss and proposes an Adversarial Restricted Boltzmann Machine (ARBM) and an Adversarial Deep Boltzmann Machine (ADBM). Different from the other RBMs, an ARBM minimizes its adversarial loss between the data distribution and its model distribution without explicit gradients. Different from traditional DBMs, an ADBM minimizes its adversarial loss without a layer-by-layer pre-training. In order to generate highquality color images, this paper proposes an Adversarial Hybrid Deep Generative Net (AHDGN) based on an ADBM. The experiments verify that the adversarial loss can be minimized in our proposed models, and the generated images are comparable with the current state-of-the-art results.

High Quality Color Image Compression for Discrete Transform Domain Downward Conversion Block Based Image Coding

Text and image data are important elements for information processing almost in all the computer applications. Uncompressed image or text data require high transmission bandwidth and significant storage capacity. Designing and compression scheme is more critical with the recent growth of computer applications. Among the various spatial domain image compression techniques, multi-level Block partition Coding (ML-BTC) is one of the best methods which has the least computational complexity. The parameters such as Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are measured and it is found that the implemented methods of BTC are superior to the traditional BTC. This paves the way for a nearly error free and compressed transmission of the images through the communication channel.

Efficient storage and classification of color patterns based on integrating interpolation with ANN/SVM

Color images usually have large storage sizes as they are composed of three planes in the raw image format represented with the red, green, and blue components. Traditional color image compression schemes can be used to save the storage size of the color images. Unfortunately, most of these schemes are lossy in nature, which affects the details of color images. This paper presents a different treatment to the color image storage problem depending on the original color image formation process. In the color image formation process, not all the red, green, and blue components of the color images are acquired, simultaneously. Only, one component at each pixel position is acquired and Color Filter Array (CFA) interpolation is used to estimate the other two components using interpolation algorithms like Minimized-Laplacian Residual Interpolation (MLRI) and Linear Interpolation with Laplacian Second Order Correction (LILSOC). We adopt a similar strategy in this paper for reducing the storage sizes of color images by 66.67% of their original sizes. The sensitivity of the pattern recognition process to the proposed color image storage and interpolation strategy is studied in this paper. A cepstral feature extraction algorithm is adopted in this paper for extracting features from the interpolated patterns for further classification. Moreover, two types of classifiers are considered and compared in this paper for the pattern recognition: Artificial Neural Networks (ANNs), and Support Vector Machines (SVMs). Simulation results reveal the success of the proposed strategy for color image storage and interpolation in obtaining high-quality color images in addition to the high Recognition Rates (RR) of color patterns after interpolation. This success encourages the use of the proposed color image storage strategy in storing large volumes of color databases.

An Efficient Noise Separation Technique for Removal of Gaussian and Mixed Noises in Monochrome and Color Images

Images are often affected by different kinds of noise while acquiring, storing and transmitting it. Even the datasets gathered by the various image acquiring devices would be contaminated by noise. Hence, there is a need for noise reduction in the image, often called Image De-noising and thereby it becomes the significant concerns and fundamental step in the area of image processing. During image de-noising, the big challenge before the researchers is removing noise from the original image in such a way that most significant properties like edges, lines, etc., of the image, should be preserved. There were various published algorithms and techniques to de-noise the image and every single approach has its own limitations, benefits, and assumptions. This paper reviews the noise models and presents a comparative analysis of various de-noising filters that works for color images with single and mixed noises. It also suggests the best filter for color that involve in producing a high-quality color image. The metrics like PSNR, Entropy, SSIM, MSE, FSIM, and EPI are considered as image quality assessment metric

Development of High Quality Color Image Compression using Block Transformation

The upcoming era of social media will be highly equipped with the images and the videos. Images or multimedia content sharing and storing services are still costly for common man which is need to be resolved to cover all the users either middle class or high society users. Various online platforms have filled the gaps for freedom of expression for everyone. demand of the multimedia data sharing telecommunication networks has increased. images has changed the requirements for effective transmission and storage media. With the convenience of accessibility of press tools and digital image web exchange, there has been a dramatic increase. Image is the least component of multimedia information and includes a important portion of the velocity of communication for multimedia data Developments in image compression techniques have therefore developed potential requirement. For all pictures, a fundamental concept of image formation is that the pixels are linked and comprise extremely useless data afterwards. The primary objective of this job is to discover in the image reduced associated pixel intensities. In this work an adaptive frequency domain block processing for color image compression has developed and simulated.

Arts of Global Africa

Arts of Global Africa

Learning a Deep Convolutional Network for Colorization in Monochrome-Color Dual-Lens System

In the monochrome-color dual-lens system, the gray image captured by the monochrome camera has better quality than the color image from the color camera, but does not have color information. To get high-quality color images, it is desired to colorize the gray image with the color image as reference. Related works usually use hand-crafted methods to search for the best-matching pixel in the reference image for each pixel in the input gray image, and copy the color of the best-matching pixel as the result. We propose a novel deep convolution network to solve the colorization problem in an end-to-end way. Based on our observation that, for each pixel in the input image, there usually exist multiple pixels in the reference image that have the correct colors, our method performs weighted average of colors of the candidate pixels in the reference image to utilize more candidate pixels with correct colors. The weight values between pixels in the input image and the reference image are obtained by learning a weight volume using deep feature representations, where an attention operation is proposed to focus on more useful candidate pixels and a 3-D regulation is performed to learn with context information. In addition, to correct wrongly colorized pixels in occlusion regions, we propose a color residue joint learning module to correct the colorization result with the input gray image as guidance. We evaluate our method on the Scene Flow, Cityscapes, Middlebury, and Sintel datasets. Experimental results show that our method largely outperforms the state-of-the-art methods.

High Quality Color Image Compression using DWT and Multi-level Block Partition Encoding-Decoding Technique

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.

Breast firmness is of greater importance for women's attractiveness than breast size.

Male preferences are believed to have played a role in the evolution of permanently enlarged breasts in human females. Although breast size and shape are proven to affect women's attractiveness, their relative importance has not been investigated thus far. We aime to address this gap. We prepared two sets of stimuli, each comprising 49 high-quality color images of a topless woman in a three-quarter view that varied in breast size (from 1 to 7) and firmness (from 1 to 7). Set A depicted the glandular ptosis (ie, breast shape being manipulated but the nipple always directed forward), while Set B depicted true ptosis (both breast shape and nipple position being manipulated). Participants (aged 18-45) were assigned to Set A (62 women, 60 men) or Set B (76 women, 52 men). First, each participant indicated the most attractive woman in the set of images. Next, the participant chose the more attractive woman from pairs in which one female deviated from the participant's ideal in breast size and the other in breast firmness. Both men and women preferred breasts of average or slightly above-average size and high or extreme firmness. Glandular ptosis was as important for attractiveness as breast size, but true ptosis was of much greater importance. Men preferred slightly bigger breasts than women. Further attempts to explain evolution of permanent breasts in human females should give greater attention to breast shape than has previously been the case.

High-Quality Color Image Compression by Quantization Crossing Color Spaces

Coding of a color image usually happens in the YCbCr space so that the rate-distortion optimization is conducted in this space. Due to the use of a non-unitary matrix in the RGB-to-YCbCr conversion, an optimal coding performance achieved in the YCbCr space does not guarantee an optimal quality in the RGB space, which would impact most display devices that need RGB signals as the inputs. In this paper, we first study the relationship between the coding distortions of the compressed RGB signals and the quantization errors occurred in the coded YCbCr signals. Then, we design a new quantization scheme crossing the RGB and YCbCr spaces to achieve a high-quality color image compression with the YCbCr 4:4:4 format. Although our proposed quantization takes place in the YCbCr space, it aims at reducing the coding distortion in the RGB space as much as possible. Experimental results demonstrate that our proposed method offers a significant quality gain over the existing block-based coding methods for various images.

A Color Image Segmentation Method Based on Region Salient Color and Fuzzy C-Means Algorithm

This paper proposes a color image segmentation method based on region salient color and the fuzzy C-means (FCM) algorithm. The method first uses the convex hull theory based on Harris corner detection to detect the object of the image. Thus, the object and the background can be separated. Then, the quantized color histogram can be studied in the HSV color space. By calculating the number of the peak values of both the object and the background histograms, the quantity of the regional salient colors can be obtained. The quantity is the number of the clustering centroids of FCM algorithm. Finally, the FCM algorithm and the noise correction algorithm can be used in the object and the background, respectively. The obtained segmented image consists of the object and the background segmentation. It proves that the method in this paper is an effective segmentation method based on the experiments made by use of Berkeley segmentation dataset. According to the experimental results, it can be concluded that the proposed algorithm has the highest segmentation accuracy and the shortest computing time among the algorithms mentioned in this paper. The algorithm can achieve high-quality, stable and accurate color image segmentation results.

Shoot high-quality color images using dual-lens system with monochrome and color cameras

In the dual-lens system with monochrome and color cameras, the gray image captured by the monochrome camera has better quality than the color image from the color camera, but does not have color information. To get high-quality color images, it is desired to colorize the gray image with the color image as reference. Due to occlusions, the colorization will inevitably fail in some cases. Thus, evaluating the colorization quality is also of great importance. We solve both problems in this paper. For colorization, we propose a gray-color correspondence prior, i.e. in local regions, if two patches are similar in the gray channel, it is very often that the two pixels centered at these two patches have similar colors. Based on this prior, a deep learning based and coarse-to-fine colorization method is proposed. For evaluating the colorization quality, we propose a symmetry colorization based evaluation method. Experimental results show that our method could largely outperform the state-of-the-art methods and is also efficient in computation.

Italian norms and naming latencies for 357 high quality color images.

In the domain of cognitive studies on the lexico-semantic representational system, one of the most important means of ensuring effective experimental designs is using ecological stimulus sets accompanied by normative data on the most relevant variables affecting the processing of their items. In the context of image sets, color photographs are particularly suited to this purpose as they reduce the difficulty of visual decoding processes that may emerge with traditional image sets of line drawings. This is especially so in clinical populations. In this study we provide Italian norms for a set of 357 high quality image-items belonging to 23 semantic subcategories from the Moreno-Martínez and Montoro database. Data from several variables affecting image processing were collected from a sample of 255 Italian-speaking participants: age of acquisition, familiarity, lexical frequency, manipulability, name agreement, typicality and visual complexity. Lexical frequency data were derived from the CoLFIS corpus. Furthermore, we collected data on image oral naming latencies to explore how the variance in these latencies could be explained by these critical variables. Multiple regression analyses on the naming latencies show classical psycholinguistic phenomena, such as the effects of age of acquisition and name agreement. In addition, manipulability was also a significant predictor. The described Italian normative data and naming latencies are available for download as supplementary material.