The noise level of an image depends on settings of an imaging device. The settings can be used to select appropriate parameters for denoising methods. But denoising methods based on deep convolutional neural networks (deep-CNN) do not have such adjustable parameters. Therefore, a deep-CNN whose training data contain limited levels of noise does not effectively restore images whose noise level is different from the training data. If the range of noise levels of training data is extended to solve the problem, the maximum performance of a produced deep-CNN is limited. To solve the tradeoff, we propose a deep-CNN that is adjustable to the noise level of the input image immediately. We use soft shrinkage for activation functions of our deep-CNN. The soft shrinkage has thresholds proportional to the noise level given by the user. We also propose an optimization method for proportionality coefficients for the thresholds of soft shrinkage. Our method optimizes the coefficients for various noise levels simultaneously. In our experiment using a test set whose noise level is from 5 to 50, the proposed method showed higher PSNR than that in the case of the conventional method using only one deep-CNN, and PSNR comparable to that in the case of the conventional method using multiple noise-level-specific CNNs.

In this paper, we propose a novel feature localization method based on a global vector concentration approach. Our approach does not rely on the detection of local salient features around feature points. Instead, we exploit global structural information of the object extracted by calculating the concentration of directional vectors from sampling points. Those vectors are combined with local pattern descriptors of a query image and selected from preliminarily trained extended templates by nearest neighbor search. Due to the insensitivity of local changes, our method can handle partially occluded and noisy objects. We apply the proposed method to fully automatic feature localization of the left ventricular in echocardiograms. The results show the effectiveness of our method in comparison with a conventional edge-based method in terms of accuracy and robustness.