103 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
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
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