Light Field Saliency Detection with Deep Convolutional Networks Jun Zhang, Yamei Liu, Shengping Zhang, Ronald Poppe, and Meng Wang
1 Light Field Saliency Detection with Deep Convolutional Networks Jun Zhang, Yamei Liu, Shengping Zhang, Ronald Poppe, and Meng Wang Abstract—Light field imaging presents an attractive alternative � � � �, �, �∗, �∗ … to RGB imaging because of the recording of the direction of the � incoming light. The detection of salient regions in a light field �� �, �, �1, �1 �� �, �, �1, �540 … image benefits from the additional modeling of angular patterns. … For RGB imaging, methods using CNNs have achieved excellent � � results on a range of tasks, including saliency detection. However, �� �, �, ��, �� Micro-lens … it is not trivial to use CNN-based methods for saliency detection Main lens Micro-lens Photosensor image on light field images because these methods are not specifically array �� �, �, �375, �1 �� �, �, �375, �540 (a) (b) designed for processing light field inputs. In addition, current � � ∗ ∗ light field datasets are not sufficiently large to train CNNs. To �� � , � , �, � … overcome these issues, we present a new Lytro Illum dataset, �� �−4, �−4, �, � �� �−4, �4, �, � … which contains 640 light fields and their corresponding ground- … truth saliency maps. Compared to current light field saliency � � � � , � , �, � datasets [1], [2], our new dataset is larger, of higher quality, � 0 0 … contains more variation and more types of light field inputs. Sub-aperture Main lens Micro-lens Photosensor image This makes our dataset suitable for training deeper networks array �� �4, �−4, �, � �� �4, �4, �, � and benchmarking. Furthermore, we propose a novel end-to- (c) (d) end CNN-based framework for light field saliency detection. Specifically, we propose three novel MAC (Model Angular Fig. 1. Illustrations of light field representations. (a) Micro-lens image Changes) blocks to process light field micro-lens images.
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