scholarly journals Spatially Adaptive Image Denoising via Enhanced Noise Detection Method for Grayscale and Color Images

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 112985-113002
Author(s):  
Amandeep Singh ◽  
Gaurav Sethi ◽  
G. S. Kalra
Keyword(s):  
Image Denoising ◽  
Detection Method ◽  
Color Images ◽  
Noise Detection ◽  
Spatially Adaptive

Research on Several Key Techniques of Power Tool Rest Noise Detection System

2012 ◽  
Vol 630 ◽  
pp. 271-275
Author(s):  
Xiao Hong Lu ◽  
Yong Yan Shang ◽  
Peng Zhuo Han ◽  
Guang Jun Li ◽  
Wen Yi Wu
Keyword(s):  
Spectrum Analysis ◽  
Detection Method ◽  
Detection System ◽  
Sampled Data ◽  
Noise Detection ◽  
Machining Center ◽  
Cnc Lathe ◽  
Spectrum Transformation ◽  
Key Techniques

The scarcity and imperfection of power tool rest noise detection method have seriously limited the development of the industry of CNC lathe, lathe and milling composite machining center. A noise detection system based on LabVIEW is developed. The developed system adopts a noise sensor as noise detection component to test the noise information of the power tool rest. To enhance the anti-interference ability of this system, the sampled signals are amplified and adjusted by the signal disposal instrument. Through the spectrum transformation and spectrum analysis of the sampled noise signals, the noise causes of the power tool rest can be inquired and the concerned measurements can be taken to reduce the noise effectively. Finally, the sampled data is stored by the data saving function.

scholarly journals From Insect Vision to a Novel Bio-Inspired Algorithm for Image Denoising

2020 ◽  
Author(s):  
Manfred Hartbauer
Keyword(s):  
Image Denoising ◽  
Color Images ◽  
Insect Vision ◽  
Night Vision ◽  
Primitive Type ◽  
Noise Estimate ◽  
Local Means ◽  
Static Images ◽  
Image Details ◽  
Novel Algorithm

Night active insects inspired the development of image enhancement methods that uncover the information contained in dim images or movies. Here, I describe a novel bionic night vision (NV) algorithm that operates in the spatial domain to remove noise from static images. The parameters of this NV algorithm can be automatically derived from global image statistics and a primitive type of noise estimate. In a first step, luminance values were ln-transformed, and then adaptive local means’ calculations were executed to remove the remaining noise without degrading fine image details and object contours. Its performance is comparable with several popular denoising methods and can be applied to grey-scale and color images. This novel algorithm can be executed in parallel at the level of pixels on programmable hardware.

Spatially adaptive image denoising based on joint image statistics in the curvelet domain

2006 ◽  
Author(s):  
L. Tessens ◽  
A. Pižurica ◽  
A. Alecu ◽  
A. Munteanu ◽  
W. Philips
Keyword(s):  
Image Denoising ◽  
Image Statistics ◽  
Spatially Adaptive

Spatially adaptive context-based wavelet shrinkage for borescope image denoising

2006 ◽  
Author(s):  
Peng Ding ◽  
Qi Shuang Ma ◽  
Chang You Li ◽  
Hong Yu Yao
Keyword(s):  
Image Denoising ◽  
Wavelet Shrinkage ◽  
Spatially Adaptive

Edge Detection of Color Images Based on Improved Morphological Gradient Operators

2014 ◽  
Vol 511-512 ◽  
pp. 550-553 ◽  
Author(s):  
Jian Yong Liang
Keyword(s):  
Edge Detection ◽  
Detection Method ◽  
Color Image ◽  
Color Images ◽  
Morphological Gradient ◽  
Image Edge Detection ◽  
Gradient Based ◽  
Image Edge ◽  
Edge Detection Method ◽  
Traditional Approaches

Edge detection is an old and hot topic in image processing, pattern recognition and computer vision. Numerous edge detection approaches have been proposed to gray images. It is difficult to extend these approaches to color image edge detection. A novel edge detection method based on mathematical morphology for color images is proposed in this paper. The proposed approach firstly compute vector gradient based on morphological gradient operators, and then compute the optimal gradient according to structure elements with different size. Finally, we use a threshold to binary the gradient images and then obtain the edge images. Experimental results show that the proposed approach has advantages of suppressing noise and preserving edge details and it is not sensitive to noise pixel. The finally edge images via the proposed method have high PSNR and NC compared with the traditional approaches.

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