Due to strong learning ability, convolutional neural networks (CNNs) have been developed in image denoising. However, convolutional operations may change original distributions of noise in corrupted images, which may increase training difficulty in image denoising. Using relations of surrounding pixels can effectively resolve this problem. Inspired by that, we propose a robust deformed denoising CNN (RDDCNN) in this paper. The proposed RDDCNN contains three blocks: a deformable block (DB), an enhanced block (EB) and a residual block (RB). The DB can extract more representative noise features via a deformable learnable kernel and stacked convolutional architecture, according to relations of surrounding pixels. The EB can facilitate contextual interaction through a dilated convolution and a novel combination of convolutional layers, batch normalisation (BN) and ReLU, which can enhance the learning ability of the proposed RDDCNN. To address long‐term dependency problem, the RB is used to enhance the memory ability of shallow layer on deep layers and construct a clean image. Besides, we implement a blind denoising model. Experimental results demonstrate that our denoising model outperforms popular denoising methods in terms of qualitative and quantitative analysis. Codes can be obtained at https://github.com/hellloxiaotian/RDDCNN .

Deep convolutional neural networks (CNNs) have been popularly adopted in image super-resolution (SR). However, deep CNNs for SR often suffer from the instability of training, resulting in poor image SR performance. Gathering complementary contextual information can effectively overcome the problem. Along this line, we propose a coarse-to-fine SR CNN (CFSRCNN) to recover a high-resolution (HR) image from its low-resolution version. The proposed CFSRCNN consists of a stack of feature extraction blocks (FEBs), an enhancement block (EB), a construction block (CB) and, a feature refinement block (FRB) to learn a robust SR model. Specifically, the stack of FEBs learns the long- and short-path features, and then fuses the learned features by expending the effect of the shallower layers to the deeper layers to improve the representing power of learned features. A compression unit is then used in each FEB to distill important information of features so as to reduce the number of parameters. Subsequently, the EB utilizes residual learning to integrate the extracted features to prevent from losing edge information due to repeated distillation operations. After that, the CB applies the global and local LR features to obtain coarse features, followed by the FRB to refine the features to reconstruct a high-resolution image. Extensive experiments demonstrate the high efficiency and good performance of our CFSRCNN model on benchmark datasets compared with state-of-the-art SR models. The code of CFSRCNN is accessible on https://github.com/hellloxiaotian/CFSRCNN .