1,553 research outputs found
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
Multi-Task Learning Approach for Natural Images' Quality Assessment
Blind image quality assessment (BIQA) is a method to predict the quality of a natural image without the presence of a reference image. Current BIQA models typically learn their prediction separately for different image distortions, ignoring the relationship between the learning tasks. As a result, a BIQA model may has great prediction performance for natural images affected by one particular type of distortion but is less effective when tested on others. In this paper, we propose to address this limitation by training our BIQA model simultaneously under different distortion conditions using multi-task learning (MTL) technique. Given a set of training images, our Multi-Task Learning based Image Quality assessment (MTL-IQ) model first extracts spatial domain BIQA features. The features are then used as an input to a trace-norm regularisation based MTL framework to learn prediction models for different distortion classes simultaneously. For a test image of a known distortion, MTL-IQ selects a specific trained model to predict the image’s quality score. For a test image of an unknown distortion, MTLIQ first estimates the amount of each distortion present in the image using a support vector classifier. The probability estimates are then used to weigh the image prediction scores from different trained models. The weighted scores are then pooled to obtain the final image quality score. Experimental results on standard image quality assessment (IQA) databases show that MTL-IQ is highly correlated with human perceptual measures of image quality. It also obtained higher prediction performance in both overall and individual distortion cases compared to current BIQA models
Interpretable Hyperspectral AI: When Non-Convex Modeling meets Hyperspectral Remote Sensing
Hyperspectral imaging, also known as image spectrometry, is a landmark
technique in geoscience and remote sensing (RS). In the past decade, enormous
efforts have been made to process and analyze these hyperspectral (HS) products
mainly by means of seasoned experts. However, with the ever-growing volume of
data, the bulk of costs in manpower and material resources poses new challenges
on reducing the burden of manual labor and improving efficiency. For this
reason, it is, therefore, urgent to develop more intelligent and automatic
approaches for various HS RS applications. Machine learning (ML) tools with
convex optimization have successfully undertaken the tasks of numerous
artificial intelligence (AI)-related applications. However, their ability in
handling complex practical problems remains limited, particularly for HS data,
due to the effects of various spectral variabilities in the process of HS
imaging and the complexity and redundancy of higher dimensional HS signals.
Compared to the convex models, non-convex modeling, which is capable of
characterizing more complex real scenes and providing the model
interpretability technically and theoretically, has been proven to be a
feasible solution to reduce the gap between challenging HS vision tasks and
currently advanced intelligent data processing models
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