Iterative Gradient Encoding Network with Feature Co-Occurrence Loss for Single Image Reflection Removal
Removing undesired reflections from a photo taken in front of glass is of great importance for enhancing visual computing systems' efficiency. Previous learning-based approaches have produced visually plausible results for some reflections type, however, failed to generalize against other reflection types. There is a dearth of literature for efficient methods concerning single image reflection removal, which can generalize well in large-scale reflection types. In this study, we proposed an iterative gradient encoding network for single image reflection removal. Next, to further supervise the network in learning the correlation between the transmission layer features, we proposed a feature co-occurrence loss. Extensive experiments on the public benchmark dataset of SIR$^2$ demonstrated that our method can remove reflection favorably against the existing state-of-the-art method on all imaging settings, including diverse backgrounds. Moreover, as the reflection strength increases, our method can still remove reflection even where other state of the art methods failed.
Code (0)
등록된 구현이 없습니다.
Tasks
Reflection RemovalSimilar Papers 제목 키워드 기반
Neural Collapse Beyond the Unconstrainted Features Model: Landscape, Dynamics, and Generalization in the Mean-Field Regime
Neural Collapse is a phenomenon where the last-layer representations of a well-trained neural network converge to a highly structured geometry. In this paper, we focus on its first (and most basic) property, known as NC1…
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection
We are interested in the detection and segmentation of anomalies in images where the anomalies are typically small (i.e., a small tear in woven fabric, broken pin of an IC chip). From a statistical learning point of view…
Anomaly Detectionreinforcement-learningReinforcement Learning (RL)Semi-supervised Anomaly Detection+1Swivel: Improving Embeddings by Noticing What's Missing
We present Submatrix-wise Vector Embedding Learner (Swivel), a method for generating low-dimensional feature embeddings from a feature co-occurrence matrix. Swivel performs approximate factorization of the point-wise mut…
Probing Visual-Audio Representation for Video Highlight Detection via Hard-Pairs Guided Contrastive Learning
Video highlight detection is a crucial yet challenging problem that aims to identify the interesting moments in untrimmed videos. The key to this task lies in effective video representations that jointly pursue two goals…
Contrastive LearningHighlight DetectionRepresentation LearningCatapults in SGD: spikes in the training loss and their impact on generalization through feature learning
In this paper, we first present an explanation regarding the common occurrence of spikes in the training loss when neural networks are trained with stochastic gradient descent (SGD). We provide evidence that the spikes i…