During image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like image segmentation, target recognition, and text detection. A two-dimensional amplitude image is one of the most common image categories, which is widely used in people’s daily life and work. Research on this kind of image-denoising algorithm is a hotspot in the field of image denoising. Conventional denoising methods mainly use the nonlocal self-similarity of images and sparser representatives in the converted domain for image denoising. In particular, the three-dimensional block matching filtering (BM3D) algorithm not only effectively removes the image noise but also better retains the detailed information in the image. As artificial intelligence develops, the deep learning-based image-denoising method has become an important research direction. This review provides a general overview and comparison of traditional image-denoising methods and deep neural network-based image-denoising methods. First, the essential framework of classic traditional denoising and deep neural network denoising approaches is presented, and the denoising approaches are classified and summarized. Then, existing denoising methods are compared with quantitative and qualitative analyses on a public denoising dataset. Finally, we point out some potential challenges and directions for future research in the field of image denoising. This review can help researchers clearly understand the differences between various image-denoising algorithms, which not only helps them to choose suitable algorithms or improve and innovate on this basis but also provides research ideas and directions for subsequent research in this field.

Solar energy is a renewable, green, clean, and universal resource that has great potential in rural areas. Combining solar heating technology with building design to increase indoor thermal comfort in winter is an effective energy-saving and environmentally friendly approach. The factors affecting solar building heating mainly include two aspects; one is the lighting area of the building, and the other is the storage of building materials. By increasing the lighting area and using materials with good heat preservation and storage performance, the indoor temperature in winter can be effectively increased, and the heating time can be prolonged, thus decreasing the energy requirements of the building. In this paper, traditional houses in cold winter areas are selected as the research object, and a roof solar heating storage system is proposed. The method is to transform the opaque roof of the traditional houses into a transparent glass roof, and the thermal insulation and heat storage material HDPE is installed in the attic floorboards. The working principle of this system is to increase the amount of indoor solar radiation to raise the indoor temperature and make use of the thermal insulation performance of heat storage materials to prolong the indoor heating time. Through ANSYS software simulation, the heat transfer process, heat transfer mode, and temperature change of the system are analyzed, and the energy saving of the system is analyzed. The system can effectively raise the indoor temperature and has good energy-saving performance. The indoor temperature is raised by 5.8 °C, and the annual heat load of the building is reduced by 1361.92 kW·h, with a reduction rate of 25.02%.

Convolutional neural networks have achieved excellent results in image denoising; however, there are still some problems: (1) The majority of single-branch models cannot fully exploit the image features and often suffer from the loss of information. (2) Most of the deep CNNs have inadequate edge feature extraction and saturated performance problems. To solve these problems, this paper proposes a two-branch convolutional image denoising network based on nonparametric attention and multiscale feature fusion, aiming to improve the denoising performance while better recovering the image edge and texture information. Firstly, ordinary convolutional layers were used to extract shallow features of noise in the image. Then, a combination of two-branch networks with different and complementary structures was used to extract deep features from the noise information in the image to solve the problem of insufficient feature extraction by the single-branch network model. The upper branch network used densely connected blocks to extract local features of the noise in the image. The lower branch network used multiple dilation convolution residual blocks with different dilation rates to increase the receptive field and extend more contextual information to obtain the global features of the noise in the image. It not only solved the problem of insufficient edge feature extraction but also solved the problem of the saturation of deep CNN performance. In this paper, a nonparametric attention mechanism is introduced in the two-branch feature extraction module, which enabled the network to pay attention to and learn the key information in the feature map, and improved the learning performance of the network. The enhanced features were then processed through the multiscale feature fusion module to obtain multiscale image feature information at different depths to obtain more robust fused features. Finally, the shallow features and deep features were summed using a long jump join and were processed through an ordinary convolutional layer and output to obtain a residual image. In this paper, Set12, BSD68, Set5, CBSD68, and SIDD are used as a test dataset to which different intensities of Gaussian white noise were added for testing and compared with several mainstream denoising methods currently available. The experimental results showed that this paper’s algorithm had better objective indexes on all test sets and outperformed the comparison algorithms. The method in this paper not only achieved a good denoising effect but also effectively retained the edge and texture information of the original image. The proposed method provided a new idea for the study of deep neural networks in the field of image denoising.

It was proposed to develop a better multiscale learning dictionary picture de-noising technique. The approach improves the adaptive threshold curvilinear transform, which can divide an image into different scale information and be used to build a curvilinear multiscale learning dictionary. The method finished the dictionary and sparse coefficient updates in the picture through circular iterations and then superimposed the matching curvilinear wave domain image blocks and performed the curvilinear inverse transform to generate the denoised image. The test was carried out by adding additive Gaussian noise to a standard grayscale image, and the results revealed that the peak signal-to-noise ratio of the grayscale image de-noising result of this paper’s method was improved by 56.6% on average, and the structural similarity was improved by 0.44 on average, compared to the conventional de-noising algorithm. It was determined that the enhanced approach preserved the picture’s edge and texture information well, that image quality was greatly improved, and that the algorithm’s execution efficiency was superior to that of the conventional de-noising algorithm.