In the field of agriculture being able to identify plant diseases is extremely important as it can lead to crop loss and negatively impact food security. Detecting these diseases early on is crucial, to prevent their ...
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This study explores the application of computer vision technology to improve the efficiency of maintaining cleanliness and order in high-traffic commercial areas. Given the growing importance of automation in managing...
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—This research delves into the classic Apriori algorithm, comprehensively analyzing its concepts, properties, and existing limitations, and provides a detailed demonstration of the algorithm's execution flow thro...
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Recently, the field of image dehazing has achieved rapid development. End-to-end deep learning algorithms achieve good performance. However, we observe that many algorithms perform poorly on the task of image dehazing...
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The leading cause of visual impairment after cataract, is glaucoma and the only way to combat it is to detect it early. It is imperative to develop a system that can work effectively without a lot of equipment, qualif...
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Text-guided diffusion models have significantly advanced image editing, enabling high-quality and diverse modifications driven by text prompts. However, effective editing requires inverting the source image into a lat...
L Color cast, an aberration common in digital images, poses challenges in various imageprocessing applications, affecting image quality and visual perception. This research investigates diverse methodologies for colo...
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The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of imageprocessing, which is the elimination ...
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ISBN:
(纸本)9783031821523;9783031821530
The field of image manipulation is dynamic, exploiting a range of algorithms to analyze, manipulate and enhance digital images. Our study focuses on a crucial application of imageprocessing, which is the elimination of blind Gaussian noise in order to improve image quality and facilitate image analysis by preserving essential details. In this research, we explore the use of different convolutional neural network (CNN) architectures to tackle the problem of blind Gaussian noise, applying different noise levels, ranging from low to high. We present an in-depth comparative analysis of the three main CNN architectures: DnCNN, DRNet and RIDNet, highlighting the quantitative and qualitative experimental results of these different approaches. These methods have demonstrated remarkable performance in imageprocessing tasks, particularly denoising, using various techniques built into CNNs, such as batch normalization and residual learning. Our results show that these techniques bring significant improvements to all three CNN approaches, as evidenced by the remarkable performance observed in the experimental results. These findings underline the robustness of CNN architectures in the face of complex noise scenarios, such as the blind noise scenario addressed in our study.
The field of image encryption is studied to encrypt and hide the image effectively, and the most critical thing is whether the image can be restored without any loss of details or distortion. Classical cryptography pr...
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To address the issues of low image quality and inadequate detail features encountered in current zero-shot style transfer algorithms, we propose a new text-driven image style transfer model. The model first uses CLIP ...
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