We present a highly accurate single-image superresolution (SR) method. Our method uses a very deep convolutional network inspired by VGG-net used for ImageNet classification [19]. We find increasing our network depth shows a significant improvement in accuracy. Our final model uses 20 weight layers. By cascading small filters many times in a deep network structure, contextual information over large image regions is exploited in an efficient way. With very deep networks, however, convergence speed becomes a critical issue during training. We propose a simple yet effective training procedure. We learn residuals only and use extremely high learning rates (10 4 times higher than SRCNN [6]) enabled by adjustable gradient clipping. Our proposed method performs better than existing methods in accuracy and visual improvements in our results are easily noticeable.

We propose an image super-resolution method (SR) using a deeply-recursive convolutional network (DRCN). Our network has a very deep recursive layer (up to 16 recursions). Increasing recursion depth can improve performance without introducing new parameters for additional convolutions. Albeit advantages, learning a DRCN is very hard with a standard gradient descent method due to exploding/vanishing gradients. To ease the difficulty of training, we propose two extensions: recursive-supervision and skip-connection. Our method outperforms previous methods by a large margin.

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The medical records of inpatients with diagnoses of either ICD-9 193(malignant neoplasm of the thyroid gland) or 226(benign neoplasm of the thyroid gland) in the claims sent in by medical care institutions throughout the country, to the Korea Medical Insurance Corporation (KMIC) during the period from January 1, 1986 to December 31, 1987 were abstracted. These records were abstracted in order to identify and confirm new cases of thyroid cancer among the beneficiaries of the KMIC. Using these data, the incidence rate of thyroid cancer among Koreans was estimated as of July 1, 1986 through June 30, 1987. The crude rates were estimated to be 0.76(95% Cl: 0.63-0.87) and 3.87(95% Cl: 3.60-4.14) per 100,000 in males and females, respectively, and the cumulative rates for the age spans 0-64 and 0-74 in males were 0.06% and 1.10%, respectively. In females, those were equally 0.35%. The age-adjusted rate for the world population was 0.93 per 100,000 in males, which is one of the lowest levels in the world. However, the adjusted rate in females was 3.96 per 100,000, which is an average level and very similar to that of the Chinese in Singapore and Shanghai. A similar tendency was shown in the case of the truncated rates for the age group of 35-64, which was 1.91 per 100,000 in males and 8.82 per 100,000 in females.