TY - JOUR A2 - Kausar, Shujaat Hussain AU - Yuan, Haoxuan AU - Zeng, Qiangyu AU - He, Jianxin PY - 2022 DA - 2022/01/11 TI - Adaptive Sparse Domain Selection for Weather Radar Superresolution SP - 9685831 VL - 2022 AB - Accurate and high-resolution weather radar data reflecting detailed structure information of radar echo plays an important role in analysis and forecast of extreme weather. Typically, this is done using interpolation schemes, which only use several neighboring data values for computational approximation to get the estimated, resulting the loss of intense echo information. Focus on this limitation, a superresolution reconstruction algorithm of weather radar data based on adaptive sparse domain selection (ASDS) is proposed in this article. First, the ASDS algorithm gets a compact dictionary by learning the precollected data of model weather radar echo patches. Second, the most relevant subdictionaries are adaptively select for each low-resolution echo patches during the spare coding. Third, two adaptive regularization terms are introduced to further improve the reconstruction effect of the edge and intense echo information of the radar echo. Experimental results show that the ASDS algorithm substantially outperforms interpolation methods for ×2 and ×4 reconstruction in terms of both visual quality and quantitative evaluation metrics. SN - 1058-9244 UR - https://doi.org/10.1155/2022/9685831 DO - 10.1155/2022/9685831 JF - Scientific Programming PB - Hindawi KW - ER -