For integrated diagnosis, MRI provides various types of images related to different acquisition parameters. The change of the acquisition parameters affects noise levels of the provided image in meaningful ways. To adapt the change of the noise level, it is desirable for denoising methods to be adaptive to the noise level, but deep neural network methods are not adaptive, despite their high performance. We propose a deep convolutional neural network (CNN) adjustable to noise levels. The activation functions of the CNN use soft shrinkage whose threshold is proportional to noise level of the input image.