Image Type Computer-Generated Hologram of Shaded Object

2010 ◽  
Author(s):  
Hiroshi Yoshikawa ◽  
Takeshi Yamaguchi ◽  
Tadashi Miyahara
Keyword(s):  
Computer Generated Hologram ◽  
Image Type

scholarly journals Assessing the Impact of the Loss Function, Architecture and Image Type for Deep Learning-Based Wildfire Segmentation

2021 ◽  
Vol 11 (15) ◽  
pp. 7046
Author(s):  
Jorge Francisco Ciprián-Sánchez ◽  
Gilberto Ochoa-Ruiz ◽  
Lucile Rossi ◽  
Frédéric Morandini
Keyword(s):  
Deep Learning ◽  
Loss Function ◽  
State Of The Art ◽  
Fire Detection ◽  
Loss Functions ◽  
Wildfire Spread ◽  
Combine Information ◽  
The Impact ◽  
Image Type ◽  
Segmentation Models

Wildfires stand as one of the most relevant natural disasters worldwide, particularly more so due to the effect of climate change and its impact on various societal and environmental levels. In this regard, a significant amount of research has been done in order to address this issue, deploying a wide variety of technologies and following a multi-disciplinary approach. Notably, computer vision has played a fundamental role in this regard. It can be used to extract and combine information from several imaging modalities in regard to fire detection, characterization and wildfire spread forecasting. In recent years, there has been work pertaining to Deep Learning (DL)-based fire segmentation, showing very promising results. However, it is currently unclear whether the architecture of a model, its loss function, or the image type employed (visible, infrared, or fused) has the most impact on the fire segmentation results. In the present work, we evaluate different combinations of state-of-the-art (SOTA) DL architectures, loss functions, and types of images to identify the parameters most relevant to improve the segmentation results. We benchmark them to identify the top-performing ones and compare them to traditional fire segmentation techniques. Finally, we evaluate if the addition of attention modules on the best performing architecture can further improve the segmentation results. To the best of our knowledge, this is the first work that evaluates the impact of the architecture, loss function, and image type in the performance of DL-based wildfire segmentation models.

Computer-generated hologram of asymmetry fractional Fourier transform

2005 ◽  
Author(s):  
Zhaoxuan Sheng ◽  
Hongxia Wang ◽  
Junfa He ◽  
Youjie Zhou ◽  
Jun Wang ◽  
...  
Keyword(s):  
Fourier Transform ◽  
Fractional Fourier Transform ◽  
Computer Generated Hologram

New method for making array of computer-generated hologram

2000 ◽  
Author(s):  
Toshio Honda ◽  
Tomiko Suzuki ◽  
Masami Takano
Keyword(s):  
New Method ◽  
Computer Generated Hologram

scholarly journals Image Denoising Using Discrete Wavelet Transform : A Theoretical Framework

2018 ◽  
Vol 7 (2.16) ◽  
pp. 120
Author(s):  
Praveen Bhargava ◽  
Shruti Choubey ◽  
Rakesh Kumar Bhujade ◽  
Nilesh Jain
Keyword(s):  
Image Denoising ◽  
Ultra Sound ◽  
Discrete Wavelet ◽  
Transform Domain ◽  
Hybrid Filter ◽  
Edge Sharpness ◽  
In Series ◽  
Salt And Pepper ◽  
Image Type

Noise is a random variation in brightness and color in image or simply we can say that unwanted signals are called noise. The noise is mixed with original signal and cause may troubles. Due to the presence of noise, quality of image is reduced and other features like edge sharpness and pattern recognition are badly affected. In image denoising methods to improve the results a hybrid filter is used for better visualization. The hybrid filter is composed with the combination of three filters connected in series. The hybridization has performed much better in case of salt and pepper type of noise and for most of the medical image type, either MRI, CT, SPECT, Ultra Sound. PSNR values show major improvement in comparison of other existing methods. Future, the results obtained from the presented denoising experiments would be tried to be improved further by using this method with other transform domain methods. Finally, the results are concluded that the proposed approach in terms of PSNR, MSE improvement is outperformed. 

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