The ionosphere profoundly impacts our daily lives, with ionograms serving as essential tools for assessing its state. However, due to the ionosphere’s unstable nature, these ionograms are often complex, highly variable, and plagued by various forms of noise and signal interference. While existing methods for ionogram analysis have focused on technical and model innovations, they have largely ignored the importance of improving the quality of the ionograms themselves. The basic principle ”Quality in, quality out” aptly describes this situation: the quality of the input ionograms fundamentally limits the best performance of any analysis. By enhancing data quality, we can boost the potential of analytic methods. To this end, we define ionogram reconstruction as a process that includes denoising, enhancing weak signals, and connecting interrupted signals. We present a low-cost and high-quality ionogram reconstruction pipeline, meticulously tailored in its training strategies, model architecture, and loss functions to align with the characteristics of ionograms. Key innovations include the use of semi-supervised learning to reduce manual labeling costs, the invention of the Tanh Valve and External Residual Connection mechanisms to speed up model convergence, and the creation of a loss function with a tracking mechanism to shift model focus. Experimental results confirm that our pipeline is exceptionally well-suited for ionogram reconstruction, with every design choice proving indispensable.