Variational Deep Image Restoration

This paper presents a new variational inference framework for image restoration and a convolutional neural network (CNN) structure that can solve the restoration problems described by the proposed framework. Earlier CNN-based image restoration methods primarily focused on network architecture design or training strategy with non-blind scenarios where the degradation models are known or assumed. For a step closer to real-world applications, CNNs are also blindly trained with the whole dataset, including diverse degradations. However, the conditional distribution of a high-quality image given a diversely degraded one is too complicated to be learned by a single CNN. Therefore, there have also been some methods that provide additional prior information to train a CNN. Unlike previous approaches, we focus more on the objective of restoration based on the Bayesian perspective and how to reformulate the objective. Specifically, our method relaxes the original posterior inference problem to better manageable sub-problems and thus behaves like a divide-and-conquer scheme. As a result, the proposed framework boosts the performance of several restoration problems compared to the previous ones. Specifically, our method delivers state-of-the-art performance on Gaussian denoising, real-world noise reduction, blind image super-resolution, and JPEG compression artifacts reduction.Comment: IEEE Transactions on Image Processing (TIP 2022

The Impact of Design for consumers in the Food and Beverage Industry: Design Value and Measurement

How can one justify the investment in design? By considering the relatively frequent modifications of design aspects in the service industry, the significance of justifying design investment should be addressed. In order to be a successful service business, it is critical to manage the design resources and report the outcome appropriately. Given that the main contribution of design can be the role of adding value, this study attempted to interpret the impact of design through the concept of value. Among various value theories, this study determined to utilise Holbrook’s typology of consumer value for embedding design perspectives. Holbrook’s value typology is an emotional-based holistic understanding of value which can apprehend the root causes of the preference from the customer perspective. In this context, the application of Holbrook’s value typology can contribute to the in-depth understanding of design and be extended to the other stakeholders within a business in order to understand a service business holistically for the future study. However, the greater value for a consumer is arguably not sufficient to argue the importance of design for a business. If design contributes to the greater value, value created by design activities should lead to the greater outcomes of key business phases (such as greater customer satisfaction and loyalty). This study employed statistical approaches to confirm the positive impacts of design upon key business phases quantitatively. As a result, the key findings and contributions of this study are: (1) proposing Design Value Typology which enables a better understanding of design value from customers’ emotional causes, and (2) confirming the positive influence of design to the key business phases (in other words, the investigation about a company’s efforts for improving design elements and principles can enhance the performance of the company)

The development of sociomathematical norms in the transition to tertiary exam-oriented individualistic mathematics education in an East Asian context

This study investigates social, mathematical, and sociomathematical norms perceived by college students in an engineering mathematics course and examines the students’ sense of mathematics as signals of individual merit. Data sources include a survey and one-on-one interviews with 38 students. The findings help illustrate student perceptions of academic social norms in a large-lecture course represented by the acquisition model of learning in college, detached from communal and collaborative disciplinary practices. Findings provide insights into the local educational context of an East Asian country as a case study when exam-oriented mathematics is institutionalized as normalcy

Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold Discrimination

Recently, many convolutional neural networks for single image super-resolution (SISR) have been proposed, which focus on reconstructing the high-resolution images in terms of objective distortion measures. However, the networks trained with objective loss functions generally fail to reconstruct the realistic fine textures and details that are essential for better perceptual quality. Recovering the realistic details remains a challenging problem, and only a few works have been proposed which aim at increasing the perceptual quality by generating enhanced textures. However, the generated fake details often make undesirable artifacts and the overall image looks somewhat unnatural. Therefore, in this paper, we present a new approach to reconstructing realistic super-resolved images with high perceptual quality, while maintaining the naturalness of the result. In particular, we focus on the domain prior properties of SISR problem. Specifically, we define the naturalness prior in the low-level domain and constrain the output image in the natural manifold, which eventually generates more natural and realistic images. Our results show better naturalness compared to the recent super-resolution algorithms including perception-oriented ones.Comment: Presented in CVPR 201

Leukoaraiosis is associated with pneumonia after acute ischemic stroke

Diagnostic criteria for stroke associated pneumonia based on the CDC criteria. (DOCX 25 kb