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Branded lives : the production and consumption of meaning at work
\"Branded Lives explores the increasingly popular concept of employee branding as a new form of employment relationship based on brand representation. In doing so it examines the ways in which the production and consumption of meaning at work are increasingly mediated by the brand. This insightful collection draws on qualitative empirical studies in a range of contexts to include services, retail and manufacturing organizations\"--Page 4 of cover.
Book
Comparison of Full-Reference Image Quality Models for Optimization of Image Processing Systems
The performance of objective image quality assessment (IQA) models has been evaluated primarily by comparing model predictions to human quality judgments. Perceptual datasets gathered for this purpose have provided useful benchmarks for improving IQA methods, but their heavy use creates a risk of overfitting. Here, we perform a large-scale comparison of IQA models in terms of their use as objectives for the optimization of image processing algorithms. Specifically, we use eleven full-reference IQA models to train deep neural networks for four low-level vision tasks: denoising, deblurring, super-resolution, and compression. Subjective testing on the optimized images allows us to rank the competing models in terms of their perceptual performance, elucidate their relative advantages and disadvantages in these tasks, and propose a set of desirable properties for incorporation into future IQA models.
Journal Article
On the use of deep learning for blind image quality assessment
In this work, we investigate the use of deep learning for distortion-generic blind image quality assessment. We report on different design choices, ranging from the use of features extracted from pre-trained convolutional neural networks (CNNs) as a generic image description, to the use of features extracted from a CNN fine-tuned for the image quality task. Our best proposal, named DeepBIQ, estimates the image quality by average-pooling the scores predicted on multiple subregions of the original image. Experimental results on the LIVE In the Wild Image Quality Challenge Database show that DeepBIQ outperforms the state-of-the-art methods compared, having a linear correlation coefficient with human subjective scores of almost 0.91. These results are further confirmed also on four benchmark databases of synthetically distorted images: LIVE, CSIQ, TID2008, and TID2013.
Journal Article
Blind Image Quality Assessment: Exploring Content Fidelity Perceptibility via Quality Adversarial Learning
In deep learning-based no-reference image quality assessment (NR-IQA) methods, the absence of reference images limits their ability to assess content fidelity, making it difficult to distinguish between original content and distortions that degrade quality. To address this issue, we propose a quality adversarial learning framework emphasizing both content fidelity and prediction accuracy. The main contributions of this study are as follows: First, we investigate the importance of content fidelity, especially in no-reference scenarios. Second, we propose a quality adversarial learning framework that dynamically adapts and refines the image quality assessment process on the basis of the quality optimization results. The framework generates adversarial samples for the quality prediction model, and simultaneously, the quality prediction model optimizes the quality prediction model by using these adversarial samples to maintain fidelity and improve accuracy. Finally, we demonstrate that by employing the quality prediction model as a loss function for image quality optimization, our framework effectively reduces the generation of artifacts, highlighting its superior ability to preserve content fidelity. The experimental results demonstrate the validity of our method compared with state-of-the-art NR-IQA methods. The code is publicly available at the following website:
https://github.com/Land5cape/QAL-IQA.
Journal Article
Underwater vision enhancement technologies: a comprehensive review, challenges, and recent trends
Cameras are integrated with various underwater vision systems for underwater object detection and marine biological monitoring. However, underwater images captured by cameras rarely achieve the desired visual quality, which may affect their further applications. Various underwater vision enhancement technologies have been proposed to improve the visual quality of underwater images in the past few decades, which is the focus of this paper. Specifically, we review the theory of underwater image degradations and the underwater image formation models. Meanwhile, this review summarizes various underwater vision enhancement technologies and reports the existing underwater image datasets. Further, we conduct extensive and systematic experiments to explore the limitations and superiority of various underwater vision enhancement methods. Finally, the recent trends and challenges of underwater vision enhancement are discussed. We wish this paper could serve as a reference source for future study and promote the development of this research field.
Journal Article
PSNR vs SSIM: imperceptibility quality assessment for image steganography
Peak signal to noise ratio (PSNR) and structural index similarity (SSIM) are two measuring tools that are widely used in image quality assessment. Especially in the steganography image, these two measuring instruments are used to measure the quality of imperceptibility. PSNR is used earlier than SSIM, is easy, has been widely used in various digital image measurements, and has been considered tested and valid. SSIM is a newer measurement tool that is designed based on three factors i.e. luminance, contrast, and structure to better suit the workings of the human visual system. Some research has discussed the correlation and comparison of these two measuring tools, but no research explicitly discusses and suggests which measurement tool is more suitable for steganography. This study aims to review, prove, and analyze the results of PSNR and SSIM measurements on three spatial domain image steganography methods, i.e. LSB, PVD, and CRT. Color images were chosen as container images because human vision is more sensitive to color changes than grayscale changes. Based on the test results found several opposing findings, where LSB has the most superior value based on PSNR and PVD get the most superior value based on SSIM. Additionally, the changes based on the histogram are more noticeable in LSB and CRT than in PVD. Other analyzes such as RS attack also show results that are more in line with SSIM measurements when compared to PSNR. Based on the results of testing and analysis, this research concludes that SSIM is a better measure of imperceptibility in all aspects and it is preferable that in the next steganographic research at least use SSIM.
Journal Article
CT iterative vs deep learning reconstruction: comparison of noise and sharpness
Objectives
To compare image noise and sharpness of vessels, liver, and muscle in lower extremity CT angiography between “adaptive statistical iterative reconstruction-V” (ASIR-V) and deep learning reconstruction “TrueFidelity” (TFI).
Methods
Thirty-seven patients (mean age, 65.2 years; 32 men) with lower extremity CT angiography were enrolled between November and December 2019. Images were reconstructed with two ASIR-V (blending factor of 80% and 100% (AV-100)) and three TFI (low-, medium-, and high-strength-level (TF-H) settings). Two radiologists evaluated these images for vessels (aorta, femoral artery, and popliteal artery), liver, and psoas muscle. For quantitative analyses, conventional indicators (CT number, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR)) and blur metric values (indicating the degree of image sharpness) of selected regions of interest were determined. For qualitative analyses, the degrees of quantum mottle and blurring were assessed.
Results
The higher the blending factor in ASIR-V or the strength in TFI, the lower the noise, the higher the SNR and CNR values, and the higher the blur metric values in all structures. The SNR and CNR values of TF-H images were significantly higher than those of AV-80 images and similar to those of AV-100 images. The blur metric values in TFI images were significantly lower than those in ASIR-V images (
p
< 0.001), indicating increased sharpness. Among all the investigated image procedures, the overall qualitative image quality was best in TF-H images.
Conclusion
TF-H was the most balanced image in terms of image noise and sharpness among the examined image combinations.
Key Points
• Deep learning image reconstruction “TrueFidelity” is superior to iterative reconstruction “ASIR-V” regarding image noise and sharpness.
• The high-strength “TrueFidelity” approach generated the best image quality among the examined image reconstruction procedures.
• In iterative and deep learning CT image reconstruction, the higher the blending and strength factors, the lower the image noise and the poorer the image sharpness.
Journal Article
Hierarchical Curriculum Learning for No-Reference Image Quality Assessment
Despite remarkable success has been achieved by convolutional neural networks (CNNs) in no-reference image quality assessment (NR-IQA), there still exist many challenges in improving the performance of IQA for authentically distorted images. An important factor is that the insufficient annotated data limits the training of high-capacity CNNs to accommodate diverse distortions, complicated semantic structures and high-variance quality scores of these images. To address this problem, this paper proposes a hierarchical curriculum learning (HCL) framework for NR-IQA. The main idea of the proposed framework is to leverage the external data to learn the prior knowledge about IQA widely and progressively. Specifically, as a closely-related task with NR-IQA, image restoration is used as the first curriculum to learn the image quality related knowledge (i.e., semantic and distortion information) on massive distorted-reference image pairs. Then multiple lightweight subnetworks are designed to learn human scoring rules on multiple available synthetic IQA datasets independently, and a cross-dataset quality assessment correlation (CQAC) module is proposed to fully explore the similarities and diversities of different scoring rules. Finally, the whole model is fine-tuned on the target authentic IQA dataset to fuse the learned knowledge and adapt to the target data distribution. Experimental results show that our model achieves state-of-the-art performance on multiple standard authentic IQA datasets. Moreover, the generalization of our model is fully validated by the cross-dataset evaluation and the gMAD competition. In addition, extensive analyses prove that the proposed HCL framework is effective in improving the performance of our model.
Journal Article
Underwater image restoration and enhancement: a comprehensive review of recent trends, challenges, and applications
In recent years, underwater exploration for deep-sea resource utilization and development has a considerable interest. In an underwater environment, the obtained images and videos undergo several quality degradations resulting from light absorption and scattering, low contrast, color deviation, blurred details, and nonuniform illumination. Therefore, the restoration and enhancement of degraded images and videos are critical. Numerous techniques of image processing, pattern recognition, and computer vision have been proposed for image restoration and enhancement, but many challenges remain. This survey has been estimated to be superior to other reviews because it collects all their shortcomings and lacks and gives researchers many ideas for the future. This survey presents a comparison of the most prominent approaches in underwater image processing and analysis. It also discusses an overview of the underwater environment with a broad classification into enhancement and restoration techniques and introduces the main underwater image degradation reasons in addition to the underwater image model. The existing underwater image analysis techniques, methods, datasets, and evaluation metrics are presented in detail. Furthermore, the existing limitations are analyzed, which are classified into image-related and environment-related categories. In addition, the performance is validated on images from the UIEB dataset for qualitative, quantitative, and computational time assessment. Areas in which underwater images have recently been applied are briefly discussed. Finally, recommendations for future research are provided and the conclusion is presented.
Journal Article
Improvement of image quality at CT and MRI using deep learning
Deep learning has been developed by computer scientists. Here, we discuss techniques for improving the image quality of diagnostic computed tomography and magnetic resonance imaging with the aid of deep learning. We categorize the techniques for improving the image quality as “noise and artifact reduction”, “super resolution” and “image acquisition and reconstruction”. For each category, we present and outline the features of some studies.
Journal Article