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

Varga, Domonkos

doi:10.3390/jimaging8060173

Cited by 5 publications

(2 citation statements)

References 82 publications

(124 reference statements)

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“…Image moments have proven to be versatile tools with numerous applications. In quality assessment tasks, moments have been successfully used for the evaluation of authentically distorted images [ 39 ]. Additionally, they have also been applied to the recognition of symmetric objects [ 40 ], which can be beneficial in robotics and object recognition applications in computer vision, image compression [ 41 ], or image watermarking [ 42 ].…”

Section: Related Workmentioning

confidence: 99%

On The Potential of Image Moments for Medical Diagnosis

2023

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Medical imaging is widely used for diagnosis and postoperative or post-therapy monitoring. The ever-increasing number of images produced has encouraged the introduction of automated methods to assist doctors or pathologists. In recent years, especially after the advent of convolutional neural networks, many researchers have focused on this approach, considering it to be the only method for diagnosis since it can perform a direct classification of images. However, many diagnostic systems still rely on handcrafted features to improve interpretability and limit resource consumption. In this work, we focused our efforts on orthogonal moments, first by providing an overview and taxonomy of their macrocategories and then by analysing their classification performance on very different medical tasks represented by four public benchmark data sets. The results confirmed that convolutional neural networks achieved excellent performance on all tasks. Despite being composed of much fewer features than those extracted by the networks, orthogonal moments proved to be competitive with them, showing comparable and, in some cases, better performance. In addition, Cartesian and harmonic categories provided a very low standard deviation, proving their robustness in medical diagnostic tasks. We strongly believe that the integration of the studied orthogonal moments can lead to more robust and reliable diagnostic systems, considering the performance obtained and the low variation of the results. Finally, since they have been shown to be effective on both magnetic resonance and computed tomography images, they can be easily extended to other imaging techniques.

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Section: Related Workmentioning

confidence: 99%

On The Potential of Image Moments for Medical Diagnosis

2023

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“…Local descriptors are very useful when insufficient data are available, something that happens frequently in biomedical problems [ 28 , 29 ]. In an attempt to reduce the number of learnable parameters of a CNN model, we proposed replacing the learnable parameters of the first layers with user-specified functions (such as with the use of Gabor filter bank and Hybrid Networks) [ 30 , 31 ].…”

Section: Related Workmentioning

confidence: 99%

SIFT-CNN: When Convolutional Neural Networks Meet Dense SIFT Descriptors for Image and Sequence Classification

et al. 2022

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Despite the success of hand-crafted features in computer visioning for many years, nowadays, this has been replaced by end-to-end learnable features that are extracted from deep convolutional neural networks (CNNs). Whilst CNNs can learn robust features directly from image pixels, they require large amounts of samples and extreme augmentations. On the contrary, hand-crafted features, like SIFT, exhibit several interesting properties as they can provide local rotation invariance. In this work, a novel scheme combining the strengths of SIFT descriptors with CNNs, namely SIFT-CNN, is presented. Given a single-channel image, one SIFT descriptor is computed for every pixel, and thus, every pixel is represented as an M-dimensional histogram, which ultimately results in an M-channel image. Thus, the SIFT image is generated from the SIFT descriptors for all the pixels in a single-channel image, while at the same time, the original spatial size is preserved. Next, a CNN is trained to utilize these M-channel images as inputs by operating directly on the multiscale SIFT images with the regular convolution processes. Since these images incorporate spatial relations between the histograms of the SIFT descriptors, the CNN is guided to learn features from local gradient information of images that otherwise can be neglected. In this manner, the SIFT-CNN implicitly acquires a local rotation invariance property, which is desired for problems where local areas within the image can be rotated without affecting the overall classification result of the respective image. Some of these problems refer to indirect immunofluorescence (IIF) cell image classification, ground-based all-sky image-cloud classification and human lip-reading classification. The results for the popular datasets related to the three different aforementioned problems indicate that the proposed SIFT-CNN can improve the performance and surpasses the corresponding CNNs trained directly on pixel values in various challenging tasks due to its robustness in local rotations. Our findings highlight the importance of the input image representation in the overall efficiency of a data-driven system.

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A new methodology in constructing no-reference focus quality assessment metrics

Song¹,

Liu²

2023

Pattern Recognition

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

Cited by 5 publications

References 82 publications

On The Potential of Image Moments for Medical Diagnosis

On The Potential of Image Moments for Medical Diagnosis

SIFT-CNN: When Convolutional Neural Networks Meet Dense SIFT Descriptors for Image and Sequence Classification

A new methodology in constructing no-reference focus quality assessment metrics

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