Review: A Survey on Objective Evaluation of Image Sharpness

Zhu, Mengqiu; Yu, Lingjie; Wang, Zongbiao; Ke, Zhenxia; Zhi, Chao

doi:10.3390/app13042652

Cited by 13 publications

(7 citation statements)

References 92 publications

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“…High resolution and sharpness contribute to better image interpretation. Many systems incorporate metrics to evaluate the spatial resolution and sharpness of thermal images [1], especially critical for long-range applications where details matter. Thermal sensors can introduce various types of noise into images [2], [3].…”

Section: Introductionmentioning

confidence: 99%

Image quality assessment of thermal images for Maritime surveillance applications

Swamidoss,

Al Mansoori,

Shajahan

et al. 2024

SPIE Future Sensing Technologies 2024

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Maritime surveillance systems employing thermal imaging encounter numerous challenges, where image quality significantly affects their effective range of vision. Adverse weather conditions such as haze, fog, and smog can obscure thermal imaging scenes, complicating the detection, identification, and tracking of objects of interest. For instance, these systems must track moving ships from a considerable distance using thermal imaging, while adapting to dynamic backgrounds and various weather conditions. Image quality assessment, a crucial research area, evaluates the perceived quality of an image. Standards for quantifying images often align with human perception, adopting user-focused approaches that consider an observer's ability to perform specific tasks, as outlined in the Johnson criteria. However, in real-time maritime surveillance applications, these criteria may prove inadequate in capturing image properties. This study explores the general factors that measure the dynamic range of marine surveillance thermal images, along with specific challenges in interpreting images using various quality assessment parameters.

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Section: Introductionmentioning

confidence: 99%

Image quality assessment of thermal images for Maritime surveillance applications

Swamidoss,

Al Mansoori,

Shajahan

et al. 2024

SPIE Future Sensing Technologies 2024

View full text Add to dashboard Cite

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“…A software-based image-sharpening process is a possible method for improving image blurring, even when optical information is unknown. Typical methods include sharpening filters, unsharp masking, edge detection algorithms, high-pass filters, and methods based on deep learning, which are important for image processing [13][14][15][16][17][18]. Recent trends have focused on machine-learning-based methods, such as deep neural networks, for image quality evaluation and improvement [19,20].…”

Section: Introductionmentioning

confidence: 99%

Automatic MTF Conversion between Different Characteristics Caused by Imaging Devices

Tanaka,

Ando,

Horiuchi

2024

J. Imaging

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Depending on various design conditions, including optics and circuit design, the image-forming characteristics of the modulated transfer function (MTF), which affect the spatial resolution of a digital image, may vary among image channels within or between imaging devices. In this study, we propose a method for automatically converting the MTF to the target MTF, focusing on adjusting the MTF characteristics that affect the signals of different image channels within and between different image devices. The experimental results of MTF conversion using the proposed method for multiple image channels with different MTF characteristics indicated that the proposed method could produce sharper images by moving the source MTF of each channel closer to a target MTF with a higher MTF value. This study is expected to contribute to technological advancements in various imaging devices as follows: (1) Even if the imaging characteristics of the hardware are unknown, the MTF can be converted to the target MTF using the image after it is captured. (2) As any MTF can be converted into a target, image simulation for conversion to a different MTF is possible. (3) It is possible to generate high-definition images, thereby meeting the requirements of various industrial and research fields in which high-definition images are required.

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“…Objective image quality assessment (IQA) aims at devising mathematical and computational models which can predict digital images' perceptual quality consistently with human judgment. This field is traditionally divided into three distinct areas in relation to the availability of reference (distortion-free) images for the IQA algorithms [1]. Specifically, full-reference methods possess all information about the reference images, while reduced-reference ones have some partial knowledge about them.…”

Section: Introductionmentioning

confidence: 99%

No-Reference Image Quality Assessment Using the Statistics of Global and Local Image Features

Varga

2023

Electronics

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Methods of image quality assessment are widely used for ranking computer vision algorithms or controlling the perceptual quality of video and streaming applications. The ever-increasing number of digital images has encouraged the research in this field at an accelerated pace in recent decades. After the appearance of convolutional neural networks, many researchers have paid attention to different deep architectures to devise no-reference image quality assessment algorithms. However, many systems still rely on handcrafted features to ensure interpretability and restrict the consumption of resources. In this study, our efforts are focused on creating a quality-aware feature vector containing information about both global and local image features. Specifically, the research results of visual physiology indicate that the human visual system first quickly and automatically creates a global perception before gradually focusing on certain local areas to judge the quality of an image. Specifically, a broad spectrum of statistics extracted from global and local image features is utilized to represent the quality-aware aspects of a digital image from various points of view. The experimental results demonstrate that our method’s predicted quality ratings relate strongly with the subjective quality ratings. In particular, the introduced algorithm was compared with 16 other well-known advanced methods and outperformed them by a large margin on 9 accepted benchmark datasets in the literature: CLIVE, KonIQ-10k, SPAQ, BIQ2021, TID2008, TID2013, MDID, KADID-10k, and GFIQA-20k, which are considered de facto standards and generally accepted in image quality assessment.

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Review: A Survey on Objective Evaluation of Image Sharpness

Cited by 13 publications

References 92 publications

Image quality assessment of thermal images for Maritime surveillance applications

Image quality assessment of thermal images for Maritime surveillance applications

Automatic MTF Conversion between Different Characteristics Caused by Imaging Devices

No-Reference Image Quality Assessment Using the Statistics of Global and Local Image Features

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