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.

The multi-label recognition of damaged waste bottles has important significance in environmental protection. However, most of the previous methods are known for their poor performance, especially in regards to damaged waste bottle classification. In this paper, we propose the use of a serial attention frame (SAF) to overcome the mentioned drawback. The proposed network architecture includes the following three parts: a residual learning block (RB), a mixed attention block (MAB), and a self-attention block (SAB). The RB uses ResNet to pretrain the SAF to extract more detailed information. To address the effect of the complex background of waste bottle recognition, a serial attention mechanism containing MAB and SAB is presented. MAB is used to extract more salient category information via the simultaneous use of spatial attention and channel attention. SAB exploits the obtained features and its parameters to enable the diverse features to improve the classification results of waste bottles. The experimental results demonstrate that our proposed model exhibited good recognition performance in the collected waste bottle datasets, with eight labels of three classifications, i.e., the color, whether the bottle was damage, and whether the wrapper had been removed, as well as public image classification datasets.

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High-quality images have an important effect on high-level tasks. However, due to human factors and camera hardware, digital devices collect low-resolution images. Deep networks can effectively restore these damaged images via their strong learning abilities. However, most of these networks depended on deeper architectures to enhance clarities of predicted images, where single features cannot deal well with complex screens. In this paper, we propose a dual super-resolution CNN (DSRCNN) to obtain high-quality images. DSRCNN relies on two sub-networks to extract complementary low-frequency features to enhance the learning ability of the SR network. To prevent a long-term dependency problem, a combination of convolutions and residual learning operation is embedded into dual sub-networks. To prevent information loss of an original image, an enhanced block is used to gather original information and obtained high-frequency information of a deeper layer via sub-pixel convolutions. To obtain more high-frequency features, a feature learning block is used to learn more details of high-frequency information. The proposed method is very suitable for complex scenes for image resolution. Experimental results show that the proposed DSRCNN is superior to other popular in SR networks. For instance, our DSRCNN has obtained improvement of 0.08 dB than that of MemNet on Set5 for ×3.