107 research outputs found
Neural Gradient Regularizer
Owing to its significant success, the prior imposed on gradient maps has
consistently been a subject of great interest in the field of image processing.
Total variation (TV), one of the most representative regularizers, is known for
its ability to capture the sparsity of gradient maps. Nonetheless, TV and its
variants often underestimate the gradient maps, leading to the weakening of
edges and details whose gradients should not be zero in the original image.
Recently, total deep variation (TDV) has been introduced, assuming the sparsity
of feature maps, which provides a flexible regularization learned from
large-scale datasets for a specific task. However, TDV requires retraining when
the image or task changes, limiting its versatility. In this paper, we propose
a neural gradient regularizer (NGR) that expresses the gradient map as the
output of a neural network. Unlike existing methods, NGR does not rely on the
sparsity assumption, thereby avoiding the underestimation of gradient maps. NGR
is applicable to various image types and different image processing tasks,
functioning in a zero-shot learning fashion, making it a versatile and
plug-and-play regularizer. Extensive experimental results demonstrate the
superior performance of NGR over state-of-the-art counterparts for a range of
different tasks, further validating its effectiveness and versatility
Unsupervised Deraining: Where Contrastive Learning Meets Self-similarity
Image deraining is a typical low-level image restoration task, which aims at
decomposing the rainy image into two distinguishable layers: the clean image
layer and the rain layer. Most of the existing learning-based deraining methods
are supervisedly trained on synthetic rainy-clean pairs. The domain gap between
the synthetic and real rains makes them less generalized to different real
rainy scenes. Moreover, the existing methods mainly utilize the property of the
two layers independently, while few of them have considered the mutually
exclusive relationship between the two layers. In this work, we propose a novel
non-local contrastive learning (NLCL) method for unsupervised image deraining.
Consequently, we not only utilize the intrinsic self-similarity property within
samples but also the mutually exclusive property between the two layers, so as
to better differ the rain layer from the clean image. Specifically, the
non-local self-similarity image layer patches as the positives are pulled
together and similar rain layer patches as the negatives are pushed away. Thus
the similar positive/negative samples that are close in the original space
benefit us to enrich more discriminative representation. Apart from the
self-similarity sampling strategy, we analyze how to choose an appropriate
feature encoder in NLCL. Extensive experiments on different real rainy datasets
demonstrate that the proposed method obtains state-of-the-art performance in
real deraining.Comment: 10 pages, 10 figures, accept to 2022CVP
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
Most of the existing learning-based deraining methods are supervisedly
trained on synthetic rainy-clean pairs. The domain gap between the synthetic
and real rain makes them less generalized to complex real rainy scenes.
Moreover, the existing methods mainly utilize the property of the image or rain
layers independently, while few of them have considered their mutually
exclusive relationship. To solve above dilemma, we explore the intrinsic
intra-similarity within each layer and inter-exclusiveness between two layers
and propose an unsupervised non-local contrastive learning (NLCL) deraining
method. The non-local self-similarity image patches as the positives are
tightly pulled together, rain patches as the negatives are remarkably pushed
away, and vice versa. On one hand, the intrinsic self-similarity knowledge
within positive/negative samples of each layer benefits us to discover more
compact representation; on the other hand, the mutually exclusive property
between the two layers enriches the discriminative decomposition. Thus, the
internal self-similarity within each layer (similarity) and the external
exclusive relationship of the two layers (dissimilarity) serving as a generic
image prior jointly facilitate us to unsupervisedly differentiate the rain from
clean image. We further discover that the intrinsic dimension of the non-local
image patches is generally higher than that of the rain patches. This motivates
us to design an asymmetric contrastive loss to precisely model the compactness
discrepancy of the two layers for better discriminative decomposition. In
addition, considering that the existing real rain datasets are of low quality,
either small scale or downloaded from the internet, we collect a real
large-scale dataset under various rainy kinds of weather that contains
high-resolution rainy images.Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with
arXiv:2203.1150
Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering
Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods
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