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

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

Efficacy of two different self-expanding nitinol stents for atherosclerotic femoropopliteal arterial disease (SENS-FP trial): study protocol for a randomized controlled trial

BACKGROUND: There have been few randomized control trials comparing the incidence of stent fracture and primary patency among different self-expanding nitinol stents to date. The SMART™ CONTROL stent (Cordis Corp, Miami Lakes, Florida, United States) has a peak-to-valley bridge and inline interconnection, whereas the COMPLETE™-SE stent (Medtronic Vascular, Santa Rosa, California, United States) crowns have been configured to minimize crown-to-crown interaction, increasing the stent's flexibility without compromising radial strength. Further, the 2011 ESC (European society of cardiology) guidelines recommend that dual antiplatelet therapy with aspirin and a thienopyridine such as clopidogrel should be administered for at least one month after infrainguinal bare metal stent implantation. Cilostazol has been reported to reduce intimal hyperplasia and subsequent repeat revascularization. To date, there has been no randomized study comparing the safety and efficacy of two different antiplatelet regimens, clopidogrel and cilostazol, following successful femoropopliteal stenting. METHODS/DESIGN: The primary purpose of our study is to examine the incidence of stent fracture and primary patency between two different major representative self-expanding nitinol stents (SMART™ CONTROL versus COMPLETE™-SE) in stenotic or occlusive femoropopliteal arterial lesion. The secondary purpose is to examine whether there is any difference in efficacy and safety between aspirin plus clopidogrel versus aspirin plus cilostazol for one month following stent implantation in femoropopliteal lesions. This is a prospective, randomized, multicenter trial to assess the efficacy of the COMPLETE™-SE versus SMART™ CONTROL stent for provisional stenting after balloon angioplasty in femoropopliteal arterial lesions. The study design is a 2x2 randomization design and a total of 346 patients will be enrolled. The primary endpoint of this study is the rate of binary restenosis in the treated segment at 12 months after intervention as determined by catheter angiography or duplex ultrasound. DISCUSSION: This trial will provide powerful insight into whether the design of the COMPLETE™-SE stent is more fracture-resistant or effective in preventing restenosis compared with the SMART™ CONTROL stent. Also, it will determine the efficacy and safety of aspirin plus clopidogrel versus aspirin plus cilostazol in patients undergoing stent implantation in femoropopliteal lesions. TRIAL REGISTRATION: Registered on 2 April 2012 with the National Institutes of Health Clinical Trials Registry (ClinicalTrials.gov identifier# NCT01570803)

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. Our code and more details are available on our project page, https://github.com/JWSoh/VDIR.N

Joint High Dynamic Range Imaging and Super-Resolution from a Single Image

This paper presents a new framework for jointly enhancing the resolution and the dynamic range of an image, i.e., simultaneous super-resolution (SR) and high dynamic range imaging (HDRI), based on a convolutional neural network (CNN). From the common trends of both tasks, we train a CNN for the joint HDRI and SR by focusing on the reconstruction of high-frequency details. Specifically, the high-frequency component in our work is the reflectance component according to the Retinex-based image decomposition, and only the reflectance component is manipulated by the CNN while another component (illumination) is processed in a conventional way. In training the CNN, we devise an appropriate loss function that contributes to the naturalness quality of resulting images. Experiments show that our algorithm outperforms the cascade implementation of CNN-based SR and HDRI.Y