Comparison of Image Enhancement Techniques for Rapid Processing of Post Flood Images

Harichandana, M.; Sowmya, V.; Variyar, V. V. Sajith; Sivanpillai, Ramesh

doi:10.5194/isprs-archives-xliv-m-2-2020-45-2020

Cited by 10 publications

(3 citation statements)

References 14 publications

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“…Histogram based contrast enhancements are widely used in many computer vision application for contrast enhancement (Harichandana et al, 2020). The histogram-based methods used in this study act at global and local regions of the image while enhancing the low contrast images (Hussain et al, 2018, Patel et al, 2013.…”

Section: Image Quality Enhancementmentioning

confidence: 99%

The Effect of Contrast Enhancement on Epiphyte Segmentation Using Generative Network

Sajith Variyar,

Sowmya,

Sivanpillai

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The performance of the deep learning-based image segmentation is highly dependent on two major factors as follows: 1) The organization and structure of the architecture used to train the model and 2) The quality of input data used to train the model. The input image quality and the variety of training samples are highly influencing the features derived by the deep learning filters for segmentation. This study focus on the effect of image quality of a natural dataset of epiphytes captured using Unmanned Aerial Vehicles (UAV), while segmenting the epiphytes from other background vegetation. The dataset used in this work is highly challenging in terms of pixel overlap between target and background to be segmented, the occupancy of target in the image and shadows from nearby vegetation. The proposed study used four different contrast enhancement techniques to improve the image quality of low contrast images from the epiphyte dataset. The enhanced dataset with four different methods were used to train five different segmentation models. The segmentation performances of four different models are reported using structural similarity index (SSIM) and intersection over union (IoU) score. The study shows that the epiphyte segmentation performance is highly influenced by the input image quality and recommendations are given based on four different techniques for experts to work with segmentation with natural datasets like epiphytes. The study also reported that the occupancy of the target epiphyte and vegetation highly influence the performance of the segmentation model.

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Section: Image Quality Enhancementmentioning

confidence: 99%

The Effect of Contrast Enhancement on Epiphyte Segmentation Using Generative Network

Sajith Variyar,

Sowmya,

Sivanpillai

et al. 2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…The original dataset consists of knee osteoarthritis images with dimensions of 224 x 224. To improve visualization and analysis, we apply the CLAHE technique for image preprocessing [5]. However, considering computational constraints, we resize the preprocessed images to 64 x 64 for training the diffusion model.…”

Section: Fig 2 Generated Images For Each Classmentioning

confidence: 99%

Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images

Chowdary,

Vardhan,

Akshay

et al. 2023

Atlantis Highlights in Computer Sciences

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This research paper explores the classification of knee osteoarthritis (OA) severity levels using advanced computer vision models and augmentation techniques. The study investigates the effectiveness of data preprocessing, including Contrast-Limited Adaptive Histogram Equalization (CLAHE), and data augmentation using diffusion models. Three experiments were conducted: training models on the original dataset, training models on the preprocessed dataset, and training models on the augmented dataset. The results show that data preprocessing and augmentation significantly improve the accuracy of the models. The EfficientNetB3 model achieved the highest accuracy of 84% on the augmented dataset. Additionally, attention visualization techniques, such as Grad-CAM, are utilized to provide detailed attention maps, enhancing the understanding and trustworthiness of the models. These findings highlight the potential of combining advanced models with augmented data and attention visualization for accurate knee OA severity classification.

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“…The use of image enhancement as a preliminary step to classification or segmentation problems is applicable for many computer vision use cases [11]. Superresolution networks are CNN based networks that are trained on high resolution images to serve the purpose of noise free image interpolation [12,13].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Object Detection in Floor-plan through Super Resolution

Khare¹,

Kamal²,

HB³

et al. 2021

Preprint

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Building Information Modelling (BIM) software use scalable vector formats to enable flexible designing of floor plans in the industry. Floor plans in the architectural domain can come from many sources that may or may not be in scalable vector format. The conversion of floor plan images to fully annotated vector images is a process that can now be realized by computer vision. Novel datasets in this field have been used to train Convolutional Neural Network (CNN) architectures for object detection. Image enhancement through Super-Resolution (SR) is also an established CNN based network in computer vision that is used for converting low resolution images to high resolution ones. This work focuses on creating a multi-component module that stacks a SR model on a floor plan object detection model. The proposed stacked model shows greater performance than the corresponding vanilla object detection model. For the best case, the the inclusion of SR showed an improvement of 39.47% in object detection over the vanilla network. Data and code are made publicly available at https://github.com/rbg-research/Floor-Plan-Detection.

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Comparison of Image Enhancement Techniques for Rapid Processing of Post Flood Images

Cited by 10 publications

References 14 publications

The Effect of Contrast Enhancement on Epiphyte Segmentation Using Generative Network

The Effect of Contrast Enhancement on Epiphyte Segmentation Using Generative Network

Enhancing Knee Osteoarthritis severity level classification using diffusion augmented images

Enhanced Object Detection in Floor-plan through Super Resolution

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