Real-world image deblurring:challenges and prospects
Image deblurring is a fundamental task in computer vision that holds significant importance in various applica-tions,such as medical imaging,surveillance cameras,and satellite imagery.Over the years,image deblurring has gar-nered much research attention,leading to the development of numerous dedicated methods.However,in real-world sce-narios,the imaging process may be subject to various disturbances that can lead to complex blurring.Certain factors,such as inconsistent object motion,camera lens defocusing,pixel compression during transmission,and insufficient lighting,can lead to a range of intricate blurring phenomena that further amplify the deblurring challenges.In this case,image deblurring in real-world scenarios becomes a complex ill-posed problem,and conventional image deblurring based on simu-lated blurry degradations methods often falls short when confronted with these real-world deblurring challenges.These limi-tations are ascribed to the extent of assumptions on which these conventional methods depend.These assumptions include but are not limited to 1)traditional methods often assume that the noise in the image follows a Gaussian distribution;2)spatially invariant uniform blur assumption;and 3)independence of the blurring phenomena assumption.Although conve-nient for theoretical analysis and algorithm development,these assumptions prove to be restrictive when applied to the com-plex deblurring problems encountered in real-world scenarios.Consequently,there is a pressing need to conduct special-ized research tailored to the challenges of real-world image deblurring and to enhance the effectiveness of image restoration methods.Real-world image deblurring is an intricate task that requires the development of innovative algorithms and tech-niques that are capable of accommodating the diversity of blurring factors and the complexities present in practical environ-ments.This paper attempts to create unique deblurring solutions that can efficiently handle real-world scenarios and enhance the practical applicability of deblurring methods.One approach to addressing the above challenges is to design algorithms that are robust to various types of noise and are capable of handling non-uniform and coupled blurring effects.Additionally,machine learning and deep learning have emerged as powerful tools for addressing complex real-world deblur-ring problems.Deep learning models,such as convolution neural networks and generative adversarial networks,have shown remarkable adaptability in learning from diverse data and producing high-quality deblurred images.Furthermore,researchers are exploring the integration of multiple sensor inputs,including depth information,to improve deblurring accu-racy and effectiveness.These multi-modal approaches leverage additional data sources to disentangle complex blurring effects and enhance deblurring performance.As real-world image deblurring continues to gain attention,the research com-munity is expected to contribute valuable insights and develop innovative solutions to further improve image restoration in complex scenarios.The ongoing collaboration among researchers from different fields,including computer vision,machine learning,optics,and imaging,will likely yield breakthroughs in addressing real-world deblurring challenges.In conclu-sion,real-world image deblurring is a multifaceted problem that requires tailored solutions to overcome the limitations of conventional deblurring methods.By acknowledging the complexities of real-world blurring phenomena and harnessing the power of advanced algorithms,machine learning,and multi-modal approaches,researchers are working toward enhancing image restoration in practical,challenging environments.Despite the growing interest in real-world deblurring,there remains a dearth of comprehensive surveys on the subject.To bridge this gap,this paper conducts a systematic review of real-world deblurring problems.From the perspective of image degradation models,this paper delves into various aspects and breaks down the associated challenges into isolated blur removal methods,coupled blur removal methods,and methods for unknown blur in real-world scenarios.This paper also provides a holistic overview of the state-of-the-art research in this domain,summarizes and contrasts the strengths and weaknesses of various methods,and elucidates the challenges that hin-der further improvements in image restoration performance.This paper also offers insights into the prospects and research trends in real-world deblurring tasks and offers potential solutions to the challenges ahead,including the following:1)Shortage of paired real-world training data:Acquiring high-quality training data with blur and sharp images that accurately represent the diversity of real-world scenarios is a significant challenge.The scarcity of comprehensive,real-world datasets hinders the development of supervised deblurring tasks.To address the lack of real-world data,researchers are exploring data synthesis and unsupervised learning techniques.By generating synthetic data that simulate real-world scenarios,algo-rithms can be trained on highly diverse data,and unsupervised learning is particularly suitable for improving adaptability to real-world conditions.2)Efficiency of complex models:Recent deblurring algorithms rely on complex deep learning mod-els to achieve high-quality results.However,the models often result in computational inefficiency,making these algo-rithms impractical for real-time or resource-constrained applications.The computational overhead and memory require-ments of these models also limit their deployment in practical scenarios.Researchers are developing highly efficient model architectures,such as lightweight neural networks and model compression techniques,to strike a balance between compu-tational efficiency and deblurring performance,making them suitable for real-time applications and resource-constrained environments.3)Overemphasis on degradation metrics:Many deblurring methods prioritize optimizing quantitative met-rics related to the reconstruction of image details.While these metrics provide a quantitative measure of image quality,they may not align with the perceptual quality as perceived by the human visual system.Therefore,a narrow focus on these metrics may neglect the importance of achieving results that are visually realistic and aesthetically pleasing to human observers.There has also been a growing emphasis on perceptual quality metrics,which evaluate the visual quality of deblurred images based on human perception.Integrating these metrics into the evaluation process can help ensure that deblurred images are not only quantitatively accurate but also visually pleasing to humans.As research on real-world image deblurring continues,the above challenges are expected to be gradually addressed,thus leading to effective and practical deblurring solutions.This survey aims to provide a comprehensive understanding of the current landscape of research in real-world deblurring and offers a roadmap for further advancements in this critical area of computer vision.
NETL
NSTL
万方数据
