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%.

To solve the problem that the similarity calculation between neighbors was easily disturbed by noise in the traditional nonlocal mean (NLM) denoising algorithm, a dual-core NLM denoising algorithm based on neighborhood multifeatures and variable-size search window was proposed. The algorithm first proposed to use the eigenvalues of the structure tensor to classify the region where the target pixel points were located and used different sizes of the search window to search for similar neighborhoods for target pixel points in different categories of the region, thus effectively avoiding the problem of oversmoothing or inadequate denoising of the image caused by the use of the global size. Then, the gradient features between image blocks were defined and combined with grayscale features and spatial features to measure the similarity of neighborhood blocks, which solved the problem of noise interfering with the search of similar blocks. Then, an adaptive algorithm with GaussianSinusoidal dual kernel function and quantitative estimation of the optimal values of the filtering parameters was designed to calculate the neighborhood similarity weights to improve the accuracy of image denoising. Finally, the similarity weights were used to weight and average the search neighborhood of the target pixel points to achieve the denoising of the target pixel points. To test the effectiveness of the algorithm, denoising tests were performed using multiple standard grayscale images with different levels of Gaussian white noise added and compared with several advanced denoising algorithms. The experimental results showed that the algorithm was effective. The algorithm improved the image peak signal-to-noise ratio by more than 56.54 on average when Gaussian white noise was removed, and the structural similarity reached more than 0.701 on average. Compared with the traditional NLM algorithm and other improved algorithms, the algorithm proposed in this paper had strong denoising ability, better protection of edges and texture details, and the quality of the image was greatly improved, which had a good application prospect.

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.