scholarly journals Transform and statistical parameter-based image noise level prediction

2021 ◽  
Vol 23 (06) ◽  
pp. 663-672
Author(s):  
K. Sivakumar ◽  
Keyword(s):  
Noise Level ◽  
Statistical Parameter ◽  
Experimental Result ◽  
Noise Level Estimation ◽  
Art Methods ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
The Stability ◽  
State Of Art ◽  
Deviation Method

Noise level estimation in an image is important and useful in many image processing algorithms such as image denoising, image segmentation, and image compression. Accurately estimating the noise level without prior knowledge of the image is the major challenge of today’s research. We present an improved patch-based fast noise level estimation using DCT and standard deviation method for fast and reliable noise level estimation and the result is compared with the available state-of-art methods. Experimental result shows the proposed method provides greater accuracy, the stability and also the proposed method is an average of six times faster than that of the state- of – art methods for noise level estimation.

Microscope Measurement of Jet-Pan Based on CCD

2010 ◽  
Vol 455 ◽  
pp. 525-529
Author(s):  
Dong Xiang Shao ◽  
J. Li ◽  
Ze Sheng Lu ◽  
Guang Lin Wang
Keyword(s):  
Image Processing ◽  
Optical Microscope ◽  
Servo Valve ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
The Stability

The jet-pan is a key part of jet-pan servo valve. Required precision of jet-pan should be guaranteed in process. CCD microscope measurement is used to deals with the measurement of jet-pan in the paper. The worepiece is enlarged by the optical microscope, and a series of image processing algorithms is used to process the image. The basic dimensions and geometrical tolerance of jet-pan are measured. And the stability error is less than ±0.5μm.

scholarly journals Noise Standard Deviation Estimation for Additive White Gaussian Noise Corrupted Images using SVD Domain

2019 ◽  
Vol 8 (11) ◽  
pp. 424-431 ◽  
Keyword(s):  
Real Time ◽  
Gaussian Noise ◽  
Noise Level ◽  
Additive White Gaussian Noise ◽  
Singular Values ◽  
Experimental Result ◽  
Processing Unit ◽  
Processing Application ◽  
Noise Level Estimation ◽  
Noisy Images

During denoise an image; noise level estimation is one of the most important key factors. The accurate noise level estimation is needed before processing the image. The prior knowledge of noise level estimation is also used for restoring the image without degradation. In this proposed work, the noise level is estimated by observed singular values on noisy images. The proposed work has two new methods for addressing the main challenges of the noise level estimation.1.The tail magnitude value of the noisy images singular values has high compare with signal image. This aspect is used for estimate the noise level. 2. The visual based Gaussian noise estimation is used for preprocessing the many 2D- signals processing application which enhance the range of this work. The experimental result for this noise level estimation provides reliable and also applicable for real time images/frames and some special images such as cartoon. The proposed work is needed a simple processing unit for implementing in hardware and results are more accurate. It can be used to pre-processing all kinds of real time images.

Otoconia perfect/imperfect crystals

1994 ◽  
Vol 52 ◽  
pp. 42-43
Author(s):  
César D. Fermin ◽  
Dale Martin
Keyword(s):  
Image Processing ◽  
Room Temperature ◽  
Phosphotungstic Acid ◽  
Gallus Domesticus ◽  
Crystal Matrix ◽  
Organic Templates ◽  
Image Processing Algorithms ◽  
Processing Algorithms ◽  
Diffraction Patterns

Otoconia of higher vertebrates are interesting biological crystals that display the diffraction patterns of perfect crystals (e.g., calcite for birds and mammal) when intact, but fail to produce a regular crystallographic pattern when fixed. Image processing of the fixed crystal matrix, which resembles the organic templates of teeth and bone, failed to clarify a paradox of biomineralization described by Mann. Recently, we suggested that inner ear otoconia crystals contain growth plates that run in different directions, and that the arrangement of the plates may contribute to the turning angles seen at the hexagonal faces of the crystals.Using image processing algorithms described earlier, and Fourier Transform function (2FFT) of BioScan Optimas®, we evaluated the patterns in the packing of the otoconia fibrils of newly hatched chicks (Gallus domesticus) inner ears. Animals were fixed in situ by perfusion of 1% phosphotungstic acid (PTA) at room temperature through the left ventricle, after intraperitoneal Nembutal (35mg/Kg) deep anesthesia. Negatives were made with a Hitachi H-7100 TEM at 50K-400K magnifications. The negatives were then placed on a light box, where images were filtered and transferred to a 35 mm camera as described.

Elastic Graph-based Semi-supervised Embedding with Adaptive Loss Regression

2020 ◽  
Keyword(s):  
Image Processing ◽  
International Symposium ◽  
Fast Track ◽  
Electronic Imaging ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Fast track article for IS&T International Symposium on Electronic Imaging 2020: Image Processing: Algorithms and Systems proceedings.

scholarly journals Fractional-Order Colour Image Processing

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 457
Author(s):  
Manuel Henriques ◽  
Duarte Valério ◽  
Paulo Gordo ◽  
Rui Melicio
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Fractional Derivative ◽  
Fractional Order ◽  
Fractional Derivatives ◽  
Grey Scale ◽  
Colour Image ◽  
Colour Image Processing ◽  
Image Processing Algorithms ◽  
Processing Algorithms

Many image processing algorithms make use of derivatives. In such cases, fractional derivatives allow an extra degree of freedom, which can be used to obtain better results in applications such as edge detection. Published literature concentrates on grey-scale images; in this paper, algorithms of six fractional detectors for colour images are implemented, and their performance is illustrated. The algorithms are: Canny, Sobel, Roberts, Laplacian of Gaussian, CRONE, and fractional derivative.

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