Image Reflection Removal via Contextual Feature Fusion Pyramid and Task-Driven Regularization

Abstract

In this paper, we propose a deep neural network for single image reflection removal. More specifically, we design a convolutional-grid module and take it as the building block of a feature fusion pyramid. The module leverages the combination effect of the grid topology to create a rich ensemble of receptive fields. Embedding the modules into a pyramidal architecture further expands the coverage of receptive fields. Another benefit of the pyramid is to fuse the multi-scale features learned by the modules locating at the ascending and descending pathways. The rich diversity of features helps the neural network analyze the contexts around overlapping objects at various spatial ranges and harvest the cues for layer separation. The proposed work also exploits useful semantic cues from the hyper-column descriptors generated by a pre-trained VGG-19 model to reduce the ambiguity of layer separation. In light of the low correlation between background and reflection layers, we design a channel-correlation based conditional discriminator to penalize residual reflection. The discriminator uses channel-wise attention to screen the features that can distinguish real background images from estimated ones. This paper also presents a task-driven regularization strategy. The high sensitivity of semantic segmentation to reflection is exploited for assessing the completeness of reflection removal. Training with this regularization strategy can boost the performance of both reflection removal and high-level task. The comparison against state-of-the-art algorithms on four public benchmark datasets demonstrates that this work exhibits superior performance in handling the complex reflections in wild scenarios. The proposed network architecture is also applicable to haze removal, which is another ill-posed layer separation problem, and has shown encouraging performance.