Image denoising based on a convolution neural network (CNN) can be described as the problem of learning a mapping function from a noisy image to a clean image through an end-to-end training. We propose a multiscale parallel feature extraction module (MPFE) for CNN denoising, which integrates residual learning and dense connection. The MPFE uses convolution kernels of different sizes to adaptively extract multiple features in different scales from the input image. We introduce dense connection to connect each MPFE, which can make different features interact with each other and concatenate together, so as to fully exploit the image features. The dense connection can pass the features that carry many image details, which help reduce image distortion. Meanwhile, it can also reduce gradient disappearance and improve convergence speed. The MPFE uses residual learning to resolve the gradient loss caused by high network depth while still ensuring that the network learns the details of the noisy image. Simulation experiments show that our denoising method has the ability of suppressing Gaussian noises with different noise levels, it performs superior performance over the state-of-the-art denoising methods.