Image deblurring to improve the grain monitoring in a rice combine harvester

Rahimi-Ajdadi, Fatemeh; Mollazade, Kaveh

doi:10.1016/j.atech.2023.100219

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Exploratory research is a method used to examine questions that haven't been fully researched before. The fact that deep learning models have been trained on high-quality data has led to several studies focusing on improving image quality in order to boost model accuracy [24]. It is unclear, nevertheless, if a model can be trained with images of poor quality [4].…”

Section: Methodsmentioning

confidence: 99%

Exploratory Data Preparation and Model Training Process for Raspberry Pi-Based Object Detection Model Deployments

Kamath,

A.,

Kini

et al. 2024

IEEE Access

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Most computer vision applications that use deep learning on constrained device come from the Internet of Things (IoT) or robotics fields, where low-quality cameras are used to capture input images in real time. Since most pretrained models typically undergo training on high-quality image datasets, the low-quality, noisy, or blurry images captured with these resource-constrained devices could possibly have a negative impact on the models' performance. To determine if model performance is impacted by training models using low-quality data, a secondary image dataset named MOD-2022 was prepared for object detection and tracking tasks using an exploratory data preparation methodology. This dataset was primarily designed to include a wider range of classes with an adequate number of images per class while being free of errors due to inaccurate labelling or annotations. Additionally, a training approach is also proposed to support the model's training when the dataset is considerably large. A VGG16-SSD model was trained with this approach on the prepared dataset and deployed on a Raspberry Pi and it showed that this approach is very useful in developing models for resource-constrained applications. Furthermore, this data preparation approach can be extended to prepare numerous other datasets required for training models designed to be deployed on constrained devices similar to the Raspberry Pi.

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Section: Methodsmentioning

confidence: 99%

Exploratory Data Preparation and Model Training Process for Raspberry Pi-Based Object Detection Model Deployments

Kamath,

A.,

Kini

et al. 2024

IEEE Access

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“…Image deblurring filters are the inverse techniques, which aim to restore the sharpness of the images from the degraded images [54]. The blur algorithms are broadly classified into two main categories: blind and non-blind, depending on the availability of the Point Spread Function (PSF) information [55]. Among them, Wiener is one of most common non-blind image restoration technique of degraded image by motion blur, unfocused optic blur, noise, and linear blur [56].…”

Section: Filter Algorithmsmentioning

confidence: 99%

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Modak,

Heil,

Stein

2024

Remote Sensing

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Image preprocessing and fusion are commonly used for enhancing remote-sensing images, but the resulting images often lack useful spatial features. As the majority of research on image fusion has concentrated on the satellite domain, the image-fusion task for Unmanned Aerial Vehicle (UAV) images has received minimal attention. This study investigated an image-improvement strategy by integrating image preprocessing and fusion tasks for UAV images. The goal is to improve spatial details and avoid color distortion in fused images. Techniques such as image denoising, sharpening, and Contrast Limited Adaptive Histogram Equalization (CLAHE) were used in the preprocessing step. The unsharp mask algorithm was used for image sharpening. Wiener and total variation denoising methods were used for image denoising. The image-fusion process was conducted in two steps: (1) fusing the spectral bands into one multispectral image and (2) pansharpening the panchromatic and multispectral images using the PanColorGAN model. The effectiveness of the proposed approach was evaluated using quantitative and qualitative assessment techniques, including no-reference image quality assessment (NR-IQA) metrics. In this experiment, the unsharp mask algorithm noticeably improved the spatial details of the pansharpened images. No preprocessing algorithm dramatically improved the color quality of the enhanced images. The proposed fusion approach improved the images without importing unnecessary blurring and color distortion issues.

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“…On the other hand, applying deconvolution algorithms to improve the resolution of fuzzy pattern optical imaging is also a widely adopted imaging technique in astronomical observation, biomedicine, microscopic imaging and production [25][26][27][28][29] . At present, researchers have proposed a variety of deconvolution methods such as Wiener filter, Lucy-Richardson method and Regularization approach [30][31][32][33][34] . Luo et al used Wiener deconvolution filter to significantly improve the spatial resolution of streak tube imaging lidar system, effectively reducing edge blurring in point clouds [30] .…”

Section: Introductionmentioning

confidence: 99%

Mask Defect Detection by Combining Wiener Deconvolution and Illumination Optimization

Kunyang,

Deng,

Zhang

et al. 2024

Preprint

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In the extreme ultraviolet (EUV) lithography process, mask defect is inevitably replicated on chips hence the yield and quality of the product are directly related to the mask quality. Mask microscopy resolution is then an essential specification. In this work, a high-efficiency method for enhancing the resolution of mask defect is proposed based on illumination optimization and Wiener deconvolution. To validate this approach, we established a verification apparatus designed to achieve a theoretical resolution of 3.0 μm with visible light. Remarkably, the empirical results demonstrated that the actual resolution attained is as low as 2.5 μm, which can be extrapolated to 53 nm resolution based on the wavelength ratio to an EUV source. The verification demonstrates a significant improvement for various periodic fringes. Moreover, the augmented capability of the apparatus facilitates the identification of mask defects.

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Image deblurring to improve the grain monitoring in a rice combine harvester

Cited by 6 publications

References 15 publications

Exploratory Data Preparation and Model Training Process for Raspberry Pi-Based Object Detection Model Deployments

Exploratory Data Preparation and Model Training Process for Raspberry Pi-Based Object Detection Model Deployments

Pansharpening Low-Altitude Multispectral Images of Potato Plants Using a Generative Adversarial Network

Mask Defect Detection by Combining Wiener Deconvolution and Illumination Optimization

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