The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging

Kurimo, Eero; Lepistö, Leena; Nikkanen, Jarno; Gren, Juuso; Kunttu, Iivari; Laaksonen, Jorma

doi:10.1007/978-3-642-02230-2_9

Cited by 16 publications

(4 citation statements)

References 8 publications

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“…Blur: It is probably the most dominant source of perceptual image quality deterioration in digital imaging [ 67 ]. To quantify the emergence of the blur effect, the blur metric of Crété-Roffet et al [ 68 ], which is based on the measurements of intensity variations between neighboring pixels, was implemented due to its low computational costs.…”

Section: Materials and Methodsmentioning

confidence: 99%

No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

Varga

2022

J. Imaging

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With the development of digital imaging techniques, image quality assessment methods are receiving more attention in the literature. Since distortion-free versions of camera images in many practical, everyday applications are not available, the need for effective no-reference image quality assessment algorithms is growing. Therefore, this paper introduces a novel no-reference image quality assessment algorithm for the objective evaluation of authentically distorted images. Specifically, we apply a broad spectrum of local and global feature vectors to characterize the variety of authentic distortions. Among the employed local features, the statistics of popular local feature descriptors, such as SURF, FAST, BRISK, or KAZE, are proposed for NR-IQA; other features are also introduced to boost the performances of local features. The proposed method was compared to 12 other state-of-the-art algorithms on popular and accepted benchmark datasets containing RGB images with authentic distortions (CLIVE, KonIQ-10k, and SPAQ). The introduced algorithm significantly outperforms the state-of-the-art in terms of correlation with human perceptual quality ratings.

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Section: Materials and Methodsmentioning

confidence: 99%

No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

Varga

2022

J. Imaging

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“…The first step in the classification process involves placing photos into one of several predetermined categories. Because image processing is one of the most quickly developing technologies, the process of image capture is frequently accompanied by a number of different types of distortions throughout the process of image acquisition [5]. For instance, when we process image acquisition, there is a possibility that it will add some form of blur or distortion to the image.…”

Section: Introductionmentioning

confidence: 99%

Analyzing performance of deep learning models under the presence of distortions in identifying plant leaf disease

Sandotra

Mahajan

Abrol

et al. 2023

IJ-ICT

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<p>Convolutional neural networks (CNN) trained using deep learning (DL) have advanced dramatically in recent years. Researchers from a variety of fields have been motivated by the success of CNNs in computer vision to develop better CNN models for use in other visually-rich settings. Successes in image classification and research have been achieved in a wide variety of domains throughout the past year. Among the many popularized image classification techniques, the detection of plant leaf diseases has received extensive research. As a result of the nature of the procedure, image quality is often degraded and distortions are introduced during the capturing of the image. In this study, we look into how various CNN models are affected by distortions. Corn-maze leaf photos from the 4,188-image corn or maize leaf Dataset (split into four categories) are under consideration. To evaluate how well they handle noise and blur, researchers have deployed pre-trained deep CNN models like visual geometry group (VGG), InceptionV3, ResNet50, and EfficientNetB0. Classification accuracy and metrics like as recall and f1-score are used to evaluate CNN performance.</p>

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“…The majority of these attributes are stored in multiple databases, including (Kaggle, Image Net). The capturing of images typically contains multiple aberrations then image manipulation is a rapidly enhancing technical method [8]. For example, image procuration, it's possible that any type of noise will be present.…”

Section: Introductionmentioning

confidence: 99%

Automatic image annotation system using deep learning method to analyse ambiguous images

Al-Shammary¹,

Zaghden²,

Bouhlel³

2023

PEN

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The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging

Cited by 16 publications

References 8 publications

No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

No-Reference Quality Assessment of Authentically Distorted Images Based on Local and Global Features

Analyzing performance of deep learning models under the presence of distortions in identifying plant leaf disease

Automatic image annotation system using deep learning method to analyse ambiguous images

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