Restoration method of ancient mural image defect information based on neighborhood filtering

2021 ◽  
pp. 1-16
Author(s):  
Hao Li
Keyword(s):  
Signal To Noise Ratio ◽  
Line Drawing ◽  
Image Data ◽  
Image Noise ◽  
Local Data ◽  
Similar Information ◽  
Enhancement Method ◽  
Restoration Method ◽  
Image Defect

The local data of ancient murals is seriously damaged, and image noise exists in the process of restoration, which affects the quality of restoration of ancient murals. Therefore, this paper studies the restoration method of ancient mural image defect information based on neighborhood filtering. On the premise of obtaining the causes of ancient mural defects, this method enhances image data based on spatial domain enhancement method, extracts pixel similar information based on neighborhood filtering, searches in the whole image, and removes image noise used to repair local areas; By extracting the line drawing features of mural, the defect part of ancient mural image can be repaired. The experimental results show that the peak signal-to-noise ratio of the repaired image is the highest and the quality of the image is better under the application of the repair method.

A nonlinear signal detector for enhancement of noisy seismic record sections

Geophysics ◽  
1985 ◽  
Vol 50 (4) ◽  
pp. 539-550 ◽  
Author(s):  
S. M. Kong ◽  
Robert A. Phinney ◽  
Kabir Roy‐Chowdhury
Keyword(s):  
Signal To Noise Ratio ◽  
Line Drawing ◽  
Spatial Coherence ◽  
Complicated Structure ◽  
Signal Detector ◽  
Correlation Window ◽  
Nonlinear Signal ◽  
Incoherent Noise ◽  
Record Section

In an area of complicated structure a stacked record section is likely to be characterized by a low signal‐to‐noise ratio, even after substantial velocity analysis and other processing. The interpreter identifies signals showing phase coherence across many traces at physically allowable velocities and compiles them into a line drawing. We have developed a nonlinear filter designed to mimic this process, which passes only signals showing spatial coherence and having slowness within an allowed range. In this algorithm, called the “SSD filter,” overlapping M-trace windows are converted into a p-τ representation, obtained by multiplying the stack along the relevant slant line by the smoothed semblance. The results from all windows are composited in the p-τ domain, then retransformed into x-t. The principal tunable parameter is the width M of the correlation window, adjusted to provide an output which agrees well with the event picks made by an experienced interpreter on a test panel of data. The method was developed to enable production of cleaned‐up sections from the often noisy stacks produced in deep crustal seismic studies. An example from the COCORP Southern Appalachian profile illustrates how removal of incoherent noise from the stacked section substantially enhances the quality of the migrated section.

Analysis of color Image Enhancement Using DWT, Wavelet Shrinkageand FHE Methods

2017 ◽  
Vol 7 (8) ◽  
pp. 56
Author(s):  
Ashish Dwivedi ◽  
Nirupma Tiwari
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Human Perception ◽  
Histogram Equalization ◽  
Wavelet Shrinkage ◽  
Visual Appearance ◽  
Enhancement Method ◽  
Fuzzy Histogram ◽  
Rgb Image

Image enhancement (IE) is very important in the field where visual appearance of an image is the main. Image enhancement is the process of improving the image in such a way that the resulting or output image is more suitable than the original image for specific task. With the help of image enhancement process the quality of image can be improved to get good quality images so that they can be clear for human perception or for the further analysis done by machines.Image enhancement method enhances the quality, visual appearance, improves clarity of images, removes blurring and noise, increases contrast and reveals details. The aim of this paper is to study and determine limitations of the existing IE techniques. This paper will provide an overview of different IE techniques commonly used. We Applied DWT on original RGB image then we applied FHE (Fuzzy Histogram Equalization) after DWT we have done the wavelet shrinkage on Three bands (LH, HL, HH). After that we fuse the shrinkage image and FHE image together and we get the enhance image.

Pre-Processing of Hyperspectral Images Using Nonlinear Filters

2013 ◽  
Vol 11 (1) ◽  
pp. 8-13
Author(s):  
V. Behar ◽  
V. Bogdanova
Keyword(s):  
Signal To Noise Ratio ◽  
Correlation Coefficients ◽  
Hyperspectral Images ◽  
Spectral Correlation ◽  
Edge Preserving ◽  
Improvement Factor ◽  
Filter Output ◽  
Average Improvement ◽  
Processing Procedure

Abstract In this paper the use of a set of nonlinear edge-preserving filters is proposed as a pre-processing stage with the purpose to improve the quality of hyperspectral images before object detection. The capability of each nonlinear filter to improve images, corrupted by spatially and spectrally correlated Gaussian noise, is evaluated in terms of the average Improvement factor in the Peak Signal to Noise Ratio (IPSNR), estimated at the filter output. The simulation results demonstrate that this pre-processing procedure is efficient only in case the spatial and spectral correlation coefficients of noise do not exceed the value of 0.6

Image Quality Enhancing by Efficient Histogram Equalization

2014 ◽  
Vol 2 (2) ◽  
pp. 47-58
Author(s):  
Ismail Sh. Baqer
Keyword(s):  
Image Quality ◽  
Contrast Enhancement ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Gray Level ◽  
Signal To Noise ◽  
Squared Error ◽  
Noise Ratio

A two Level Image Quality enhancement is proposed in this paper. In the first level, Dualistic Sub-Image Histogram Equalization DSIHE method decomposes the original image into two sub-images based on median of original images. The second level deals with spikes shaped noise that may appear in the image after processing. We presents three methods of image enhancement GHE, LHE and proposed DSIHE that improve the visual quality of images. A comparative calculations is being carried out on above mentioned techniques to examine objective and subjective image quality parameters e.g. Peak Signal-to-Noise Ratio PSNR values, entropy H and mean squared error MSE to measure the quality of gray scale enhanced images. For handling gray-level images, convenient Histogram Equalization methods e.g. GHE and LHE tend to change the mean brightness of an image to middle level of the gray-level range limiting their appropriateness for contrast enhancement in consumer electronics such as TV monitors. The DSIHE methods seem to overcome this disadvantage as they tend to preserve both, the brightness and contrast enhancement. Experimental results show that the proposed technique gives better results in terms of Discrete Entropy, Signal to Noise ratio and Mean Squared Error values than the Global and Local histogram-based equalization methods

Indicators of Sustainable Development of the Province of Warmia and Mazury in the Environmental Aspect

2016 ◽  
Vol 11 (3) ◽  
pp. 299-309
Author(s):  
Mirosława Witkowska-Dąbrowska
Keyword(s):  
Quality Of Life ◽  
Sustainable Development ◽  
Data Bank ◽  
Local Data ◽  
Indicator Method ◽  
Environmental Aspect ◽  
The Sustainable Development ◽  
Assessment Indicator ◽  
Area Unit

The purpose of this study has been to identify the degree of sustainability in the development of the Province of Warmia and Mazury. The theoretical and empirical investigations were conducted between 2003-2014 based on data from the Local Data Bank. Using a comparative indicator method, 20 indicators were developed, with different directions of preference. The evaluation involves the concept of uniform preference, hence the higher the assessment indicator, the better the situation in the evaluated area unit. Our studies on the sustainable development of the Province of Warmia and Mazury suggest that the indicators measuring the environmental dimension and consequently the quality of life of the residents (in this aspect) score higher than the country's average values. It is also optimistic that some progress, however small, can be seen in this area based on the analyzed dynamics of changes.

Singular Values Decomposition and Lifting Wavelet Transform for Speech Signal Embedding into Digital Image

2019 ◽  
Vol 12 (2) ◽  
pp. 138-151
Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel
Keyword(s):  
Wavelet Transform ◽  
Speech Signal ◽  
Signal To Noise Ratio ◽  
Perceptual Quality ◽  
Lifting Wavelet Transform ◽  
Signal To Noise ◽  
Perceptual Evaluation ◽  
Lifting Wavelet ◽  
Noise Ratio

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.

scholarly journals Fast Multi-Focus Fusion Based on Deep Learning for Early-Stage Embryo Image Enhancement

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 863
Author(s):  
Vidas Raudonis ◽  
Agne Paulauskaite-Taraseviciene ◽  
Kristina Sutiene
Keyword(s):  
Deep Learning ◽  
Image Fusion ◽  
Early Stage ◽  
Image Data ◽  
Cell Detection ◽  
Processing Times ◽  
Fused Image ◽  
Stage Embryo ◽  
Early Stage Embryo

Background: Cell detection and counting is of essential importance in evaluating the quality of early-stage embryo. Full automation of this process remains a challenging task due to different cell size, shape, the presence of incomplete cell boundaries, partially or fully overlapping cells. Moreover, the algorithm to be developed should process a large number of image data of different quality in a reasonable amount of time. Methods: Multi-focus image fusion approach based on deep learning U-Net architecture is proposed in the paper, which allows reducing the amount of data up to 7 times without losing spectral information required for embryo enhancement in the microscopic image. Results: The experiment includes the visual and quantitative analysis by estimating the image similarity metrics and processing times, which is compared to the results achieved by two wellknown techniques—Inverse Laplacian Pyramid Transform and Enhanced Correlation Coefficient Maximization. Conclusion: Comparatively, the image fusion time is substantially improved for different image resolutions, whilst ensuring the high quality of the fused image.

scholarly journals 3D DCT Based Image Compression Method for the Medical Endoscopic Application

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1817
Author(s):  
Jiawen Xue ◽  
Li Yin ◽  
Zehua Lan ◽  
Mingzhu Long ◽  
Guolin Li ◽  
...  
Keyword(s):  
Image Compression ◽  
Compression Ratio ◽  
Signal To Noise Ratio ◽  
Blocking Artifacts ◽  
Compression Method ◽  
High Compression Ratio ◽  
Multiplication Operation ◽  
3D Data ◽  
Wireless Capsule

This paper proposes a novel 3D discrete cosine transform (DCT) based image compression method for medical endoscopic applications. Due to the high correlation among color components of wireless capsule endoscopy (WCE) images, the original 2D Bayer data pattern is reconstructed into a new 3D data pattern, and 3D DCT is adopted to compress the 3D data for high compression ratio and high quality. For the low computational complexity of 3D-DCT, an optimized 4-point DCT butterfly structure without multiplication operation is proposed. Due to the unique characteristics of the 3D data pattern, the quantization and zigzag scan are ameliorated. To further improve the visual quality of decompressed images, a frequency-domain filter is proposed to eliminate the blocking artifacts adaptively. Experiments show that our method attains an average compression ratio (CR) of 22.94:1 with the peak signal to noise ratio (PSNR) of 40.73 dB, which outperforms state-of-the-art methods.

scholarly journals Understanding PI-QUAL for prostate MRI quality: a practical primer for radiologists

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Francesco Giganti ◽  
Alex Kirkham ◽  
Veeru Kasivisvanathan ◽  
Marianthi-Vasiliki Papoutsaki ◽  
Shonit Punwani ◽  
...  
Keyword(s):  
Diffusion Weighted Imaging ◽  
Signal To Noise Ratio ◽  
High Signal ◽  
Diagnostic Quality ◽  
Prostate Mri ◽  
Prostate Imaging ◽  
Dynamic Contrast Enhanced ◽  
Diffusion Weighted ◽  
Contrast Enhanced

AbstractProstate magnetic resonance imaging (MRI) of high diagnostic quality is a key determinant for either detection or exclusion of prostate cancer. Adequate high spatial resolution on T2-weighted imaging, good diffusion-weighted imaging and dynamic contrast-enhanced sequences of high signal-to-noise ratio are the prerequisite for a high-quality MRI study of the prostate. The Prostate Imaging Quality (PI-QUAL) score was created to assess the diagnostic quality of a scan against a set of objective criteria as per Prostate Imaging-Reporting and Data System recommendations, together with criteria obtained from the image. The PI-QUAL score is a 1-to-5 scale where a score of 1 indicates that all MR sequences (T2-weighted imaging, diffusion-weighted imaging and dynamic contrast-enhanced sequences) are below the minimum standard of diagnostic quality, a score of 3 means that the scan is of sufficient diagnostic quality, and a score of 5 implies that all three sequences are of optimal diagnostic quality. The purpose of this educational review is to provide a practical guide to assess the quality of prostate MRI using PI-QUAL and to familiarise the radiologist and all those involved in prostate MRI with this scoring system. A variety of images are also presented to demonstrate the difference between suboptimal and good prostate MR scans.

scholarly journals Image data of crumb structure of bread from flour of czech spring wheat cultivars

2011 ◽  
Vol 22 (No. 4) ◽  
pp. 133-142 ◽  
Author(s):  
I. Švec ◽  
M. Hrušková
Keyword(s):  
Image Analysis ◽  
Image Data ◽  
Analysis Data ◽  
Wheat Cultivars ◽  
Rheological Measurements ◽  
Class A ◽  
Bread Volume ◽  
Class B ◽  
Crumb Structure

Abstract: Baking quality of flour from six wheat cultivars (harvest 2002 and 2003), belonging to the quality classes A and B, was evaluated using the fermented dough test. Analytical traits of kernel and flour showed differences between the classes which were confirmed by the baking test with the full-bread-formula according to Czech method. In addition to standard methods of the bread parameters description (specific bread volume and bread shape measurements) rheological measurements of penetrometer and image analysis were used in effort to differentiate wheat samples into the quality classes. The results of the baking test proved significant differences in specific bread volumes – the highest volume in class A was obtained with the cultivar Vinjet and in class B with SG-S1098 – approx. 410 and 420 ml/100 g. Although significant correlations among image analysis data and specific bread volume having been proved, any image analysis parameter did not distinguish the quality classes. Only the penetronetric measurements made with bread crumb were suitable for such purpose (r = 0.9083; for  = 0.01). Among image analysis data the total cell area of the crumb had the strongest correlation with specific bread volume (r = 0.7840; for α = 0.01).    

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