Linlin Zhu, Yu Han, Xiaoqi Xi, Zhicun Zhang, Mengnan Liu, Lei Li, Siyu Tan, Bin Yan*
CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3367-3386, 2023, DOI:10.32604/cmc.2023.045878
Abstract Deep learning techniques have significantly improved image restoration tasks in recent years. As a crucial component of deep learning, the loss function plays a key role in network optimization and performance enhancement. However, the currently prevalent loss functions assign equal weight to each pixel point during loss calculation, which hampers the ability to reflect the roles of different pixel points and fails to exploit the image’s characteristics fully. To address this issue, this study proposes an asymmetric loss function based on the image and data characteristics of the image recovery task. This novel loss function can adjust the weight of… More >
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