Traditional median filtering with a fixed window easily leads to edge blurring and adaptive median filtering requires manual presetting of the maximum window parameter and has insufficient retention of details when dealing with high-density salt-and-pepper noise. Aiming at these problems, this paper proposes a hybrid decision-making adaptive median filtering algorithm with dual-window detection in collaboration with particle swarm optimization (PSO). The algorithm quickly locates suspected noise points through a 3 × 3 small window and enhances noise identification accuracy by using a PSO dynamically optimized 5–35-pixel large window. Meanwhile, a hybrid decision-making mechanism based on local statistical properties was introduced to dynamically select median filtering, weighted average based on spatial distance, or pixel preservation strategy to balance noise suppression and detail preservation, and the PSO algorithm was used to automatically find the optimal parameters of the large window’s size to avoid the manual parameter-tuning process. Experiments were conducted on standard grayscale and color images and compared with four traditional methods and two more advanced methods. The experiments showed that the algorithm improved the peak signal-to-noise ratio (PSNR) value by 2–4 dB and the structural similarity index measure (SSIM) metric by 0.05–0.2 under high salt-and-pepper noise density compared with the traditional methods, which effectively improved the contradiction between noise suppression and detail retention in traditional filtering algorithms and provided a highly efficient and intelligent solution for image denoising in high-noise scenarios.

Solar energy has the advantages of being green, renewable, and energy-efficient. The use of solar energy in buildings can result in significant energy savings, and a great deal of practical and theoretical research has been conducted on solar buildings around the world. Southern Shaanxi belongs to a climate zone with hot summers and cold winters (HSCW). The mean room temperature is 4 °C, and it is lower than 2 °C at night, which greatly exceeds the thermal comfort range that the human body can bear. Aiming at a range of challenges including backward heating methods and low heating efficiency in southern Shaanxi, a fully passive thermal storage wall heating system (TSWHS) is proposed for traditional houses in the area. The specific method is to set up a thermal storage wall (TSW) outside the outer walls on the east, west, and south sides of the residential buildings. The wall is provided with an air exchange port, and there is no glass in the outer area of the doors and windows, which does not affect the normal application. The principle is that after the TSW receives solar radiation, the temperature of the internal HDPE (high-density polyethylene) and the air inside the cavity rises, which raises the interior temperature via the heat transfer and the air exchange port inside the TSW. The hot air inside the thermal wall achieves the purpose of heating. Lastly, through a comparison with the original heating system (OHS), it is confirmed that the TSWHS has certain practicability. According to an experimental simulation, the system can increase the indoor temperature by an average of 5.1 °C in winter and save about 1726.43 kWh of energy, accounting for 27.24% of the energy saving.

To address the limitations of traditional non-local mean (NLM) denoising algorithms in terms of neighborhood similarity metrics, weight calculation, and computational efficiency, this paper proposed a structural-tensor-driven and dynamic window-based fast non-local mean denoising algorithm with dual kernel weighting. First, a Gaussian–Tukey dual-kernel weighting function was designed to optimize similarity metrics. Then, spatial neighborhood features were adopted. By measuring both grayscale similarity and spatial correlation, the weight distribution rationality was further enhanced. Second, structural tensor eigenvalues were used to quantify regional structural properties. A dynamic window allocation function was designed to adaptively match search window sizes to different image regions. Finally, an integral image acceleration mechanism was proposed, significantly improving algorithm execution efficiency. Experimental results demonstrated that the proposed algorithm achieved both excellent denoising performance and edge/texture preservation capabilities. In high-noise environments, its Peak Signal-to-Noise Ratio (PSNR) outperformed the Gauss kernel non-local mean algorithm by an average of 1.96 dB, while Structural Similarity (SSIM) improved by an average of 5.7%. Moreover, the algorithm’s execution efficiency increased by approximately 7–11 times, indicating strong potential for real-time application in digital image processing.