1,180 research outputs found
Memory-guided Image De-raining Using Time-Lapse Data
This paper addresses the problem of single image de-raining, that is, the
task of recovering clean and rain-free background scenes from a single image
obscured by a rainy artifact. Although recent advances adopt real-world
time-lapse data to overcome the need for paired rain-clean images, they are
limited to fully exploit the time-lapse data. The main cause is that, in terms
of network architectures, they could not capture long-term rain streak
information in the time-lapse data during training owing to the lack of memory
components. To address this problem, we propose a novel network architecture
based on a memory network that explicitly helps to capture long-term rain
streak information in the time-lapse data. Our network comprises the
encoder-decoder networks and a memory network. The features extracted from the
encoder are read and updated in the memory network that contains several memory
items to store rain streak-aware feature representations. With the read/update
operation, the memory network retrieves relevant memory items in terms of the
queries, enabling the memory items to represent the various rain streaks
included in the time-lapse data. To boost the discriminative power of memory
features, we also present a novel background selective whitening (BSW) loss for
capturing only rain streak information in the memory network by erasing the
background information. Experimental results on standard benchmarks demonstrate
the effectiveness and superiority of our approach
Non-locally Enhanced Encoder-Decoder Network for Single Image De-raining
Single image rain streaks removal has recently witnessed substantial progress
due to the development of deep convolutional neural networks. However, existing
deep learning based methods either focus on the entrance and exit of the
network by decomposing the input image into high and low frequency information
and employing residual learning to reduce the mapping range, or focus on the
introduction of cascaded learning scheme to decompose the task of rain streaks
removal into multi-stages. These methods treat the convolutional neural network
as an encapsulated end-to-end mapping module without deepening into the
rationality and superiority of neural network design. In this paper, we delve
into an effective end-to-end neural network structure for stronger feature
expression and spatial correlation learning. Specifically, we propose a
non-locally enhanced encoder-decoder network framework, which consists of a
pooling indices embedded encoder-decoder network to efficiently learn
increasingly abstract feature representation for more accurate rain streaks
modeling while perfectly preserving the image detail. The proposed
encoder-decoder framework is composed of a series of non-locally enhanced dense
blocks that are designed to not only fully exploit hierarchical features from
all the convolutional layers but also well capture the long-distance
dependencies and structural information. Extensive experiments on synthetic and
real datasets demonstrate that the proposed method can effectively remove
rain-streaks on rainy image of various densities while well preserving the
image details, which achieves significant improvements over the recent
state-of-the-art methods.Comment: Accepted to ACM Multimedia 201
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