The Coati Optimization Algorithm (COA) is a promising metaheuristic inspired by coati hunting behaviors, demonstrating strong performance in complex optimization tasks. However, COA encounters challenges with converge...
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Coronavirus pandemic caused by a deadly virus that rapidly spread worldwide, necessitated the usage of face mask to minimize the airborne transmission of the virus. An automated face mask recognition system has made i...
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Real-time image restoration is a cutting-edge tech-nology that accelerates or rebuilds images as soon as they are captured, processed, or released to correct issues such as noise, blur, and compression. This field is ...
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With the popularization of the internet, people have paid more and more attention to imageprocessing, and deep learning has become a research hotspot. Analyzing images based on deep neural networks is a very valuable...
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The proceedings contain 8 papers. The topics discussed include: multi-scale hierarchical VQ-VAEs for blind image inpainting;intelligent monitoring of surface roughness and straightness with roundness on CNC turning ut...
ISBN:
(纸本)9798400716911
The proceedings contain 8 papers. The topics discussed include: multi-scale hierarchical VQ-VAEs for blind image inpainting;intelligent monitoring of surface roughness and straightness with roundness on CNC turning utilizing wavelet transform via neural networks;research on ECG signal denoising based on EM-UKF algorithm;an efficient framework to recognize deepfake faces using a light-weight CNN;implementation of ray tracing algorithms and their application to graphics rendering;a two-stage method with diffusion models for single-image view synthesis;enhanced potato detection using an improved YOLOv8 algorithm;and real-time cat breed identifier using OpenCV and YOLOv5.
Neural image compression employs deep neural networks and generative models to achieve impressive compression rates and reconstruction qualities compared to traditional signal-processing-based compression algorithms s...
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This research develops an algorithm for efficiently fusing multiple satellite images, addressing challenges like varying spatial resolutions, spectral bands, radiometric differences, and geometric distortions. Objecti...
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This paper presents a study on the multilayer bottleneck autoencoder applied for thumbnail color image compression task using quaternionic neural nets (QNN). Existing state-of-the-art real-valued autoencoder architect...
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image captioning consists of describing the image content with words and sentences. This task can be seen as the junction of Natural Language processing (NLP) and computer vision. Describing the visual content of imag...
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ISBN:
(纸本)9783031298592;9783031298608
image captioning consists of describing the image content with words and sentences. This task can be seen as the junction of Natural Language processing (NLP) and computer vision. Describing the visual content of images remains a difficult task because it involves both image and text processingalgorithms. To better understand this new research area, the main objective of this paper is to present an image captioning comprehensive study. In this work, the most used techniques, datasets, and evaluation metrics will be presented and discussed. At the end, a comparative study between an attention model and a transformer model for image captioning will be presented.
This paper presents a novel comparative study between two prominent compressed sensing algorithms - Orthogonal Matching Pursuit (OMP) and Iterative Hard Thresholding (IHT) - within the context of digital holography, s...
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ISBN:
(纸本)9781510673151;9781510673144
This paper presents a novel comparative study between two prominent compressed sensing algorithms - Orthogonal Matching Pursuit (OMP) and Iterative Hard Thresholding (IHT) - within the context of digital holography, specifically focusing on their efficacy in handling phase discontinuities. Previous research has predominantly centered on Gibbs ringing artifacts in image reconstruction and their mitigation. However, the aspect of phase discontinuities, which are critical in holographic imaging, has not been extensively explored. Our study implement both OMP and IHT algorithms in a simulated digital holographic environment, where phase discontinuities are inherent due to the nature of holographic imaging. We analyze how these algorithms perform in the presence of phase discontinuities. We quantitatively analyze the performance of each algorithm in handling phase discontinuities. Additionally, our study delves into the computational efficiency of both algorithms, considering their practical applicability in real-time holographic imaging systems. The results of our comparative analysis provide insights into the advantages and limitations of OMP and IHT in the context of phase discontinuities. Our findings have significant implications for advancing digital holography, particularly in applications requiring precise phase information, such as medical imaging, microscopy, and non-destructive testing.
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