977 research outputs found
Decomposition Ascribed Synergistic Learning for Unified Image Restoration
Learning to restore multiple image degradations within a single model is
quite beneficial for real-world applications. Nevertheless, existing works
typically concentrate on regarding each degradation independently, while their
relationship has been less exploited to ensure the synergistic learning. To
this end, we revisit the diverse degradations through the lens of singular
value decomposition, with the observation that the decomposed singular vectors
and singular values naturally undertake the different types of degradation
information, dividing various restoration tasks into two groups,\ie, singular
vector dominated and singular value dominated. The above analysis renders a
more unified perspective to ascribe the diverse degradations, compared to
previous task-level independent learning. The dedicated optimization of
degraded singular vectors and singular values inherently utilizes the potential
relationship among diverse restoration tasks, attributing to the Decomposition
Ascribed Synergistic Learning (DASL). Specifically, DASL comprises two
effective operators, namely, Singular VEctor Operator (SVEO) and Singular VAlue
Operator (SVAO), to favor the decomposed optimization, which can be lightly
integrated into existing convolutional image restoration backbone. Moreover,
the congruous decomposition loss has been devised for auxiliary. Extensive
experiments on blended five image restoration tasks demonstrate the
effectiveness of our method, including image deraining, image dehazing, image
denoising, image deblurring, and low-light image enhancement.Comment: 13 page
DeepOtsu: Document Enhancement and Binarization using Iterative Deep Learning
This paper presents a novel iterative deep learning framework and apply it
for document enhancement and binarization. Unlike the traditional methods which
predict the binary label of each pixel on the input image, we train the neural
network to learn the degradations in document images and produce the uniform
images of the degraded input images, which allows the network to refine the
output iteratively. Two different iterative methods have been studied in this
paper: recurrent refinement (RR) which uses the same trained neural network in
each iteration for document enhancement and stacked refinement (SR) which uses
a stack of different neural networks for iterative output refinement. Given the
learned uniform and enhanced image, the binarization map can be easy to obtain
by a global or local threshold. The experimental results on several public
benchmark data sets show that our proposed methods provide a new clean version
of the degraded image which is suitable for visualization and promising results
of binarization using the global Otsu's threshold based on the enhanced images
learned iteratively by the neural network.Comment: Accepted by Pattern Recognitio
Neural Degradation Representation Learning for All-In-One Image Restoration
Existing methods have demonstrated effective performance on a single
degradation type. In practical applications, however, the degradation is often
unknown, and the mismatch between the model and the degradation will result in
a severe performance drop. In this paper, we propose an all-in-one image
restoration network that tackles multiple degradations. Due to the
heterogeneous nature of different types of degradations, it is difficult to
process multiple degradations in a single network. To this end, we propose to
learn a neural degradation representation (NDR) that captures the underlying
characteristics of various degradations. The learned NDR decomposes different
types of degradations adaptively, similar to a neural dictionary that
represents basic degradation components. Subsequently, we develop a degradation
query module and a degradation injection module to effectively recognize and
utilize the specific degradation based on NDR, enabling the all-in-one
restoration ability for multiple degradations. Moreover, we propose a
bidirectional optimization strategy to effectively drive NDR to learn the
degradation representation by optimizing the degradation and restoration
processes alternately. Comprehensive experiments on representative types of
degradations (including noise, haze, rain, and downsampling) demonstrate the
effectiveness and generalization capability of our method
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