近年來基於深度學習的方法超在超解析度領域廣泛發展，並在基準上取得創新的性能。然而許多演算法在實際應用中性能下降，原因在於這些方法在訓練模型時大多採取固定的降級函數(例如雙三次內插)來產生訓練資料。實際應用的情況十分複雜，真實低解析度圖片可能遭受模糊和雜訊影響。因此本文提出了一個動態卷積超解析度網路(SRDC)，以適應遭受不同降級影響的低解析度圖片。 SRDC結合降級估計、去糢糊和超解析度，降級函數轉換成糢糊核特徵並引導動態卷積層提取關鍵特徵。經實驗證明SRDC能夠有效應對不同的高斯模糊，並且在多種降級數據集中具有令人滿意且合理的性能。

In recent years, deep learning-based methods have been widely developed in the field of super-resolution and have achieved innovative performance on benchmarks. However, the performance of many algorithms decreases in real applications because most of these methods use fixed degradation functions (such as bicubic interpolation) to generate training data when training models. The real application situation is very complicated, and real low-resolution images may suffer from blur and noise. Therefore, this study proposes Super-Resolution network with Dynamic Convolution (SRDC) to adapt to low-resolution images subject to different degradation effects. Different from the previous methods, SRDC combines degradation estimation, deblurring and super-resolution. The model converts the degradation function into a blur kernel feature and guides the dynamic convolutional layer to extract key features. Experiments have proved that SRDC can effectively deal with different Gaussian blurs and has satisfactory and reasonable performance in multiple degradation datasets.

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