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Removing undesired reflections from a photo taken in front of glass is ofgreat importance for enhancing visual computing systems' efficiency. Previouslearning-based approaches have produced visually plausible results for somereflections type, however, failed to generalize against other reflection types.There is a dearth of literature for efficient methods concerning single imagereflection removal, which can generalize well in large-scale reflection types.In this study, we proposed an iterative gradient encoding network for singleimage reflection removal. Next, to further supervise the network in learningthe correlation between the transmission layer features, we proposed a featureco-occurrence loss. Extensive experiments on the public benchmark dataset ofSIR$^2$ demonstrated that our method can remove reflection favorably againstthe existing state-of-the-art method on all imaging settings, including diversebackgrounds. Moreover, as the reflection strength increases, our method canstill remove reflection even where other state of the art methods failed.

Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal