scholarly journals PLANAR SCINTIGRAPHY IMAGE DE-NOISING USING COIFLET WAVELET

2021 ◽  
Vol 24 (2) ◽  
pp. 75
Author(s):  
Ayu Jati Puspitasari ◽  
Ika Cismila Ningsih ◽  
Muhammad Sulthonur Ridwan ◽  
Halim Hamadi
Keyword(s):  
Image Quality ◽  
Count Data ◽  
Reduction Method ◽  
Good Effect ◽  
Original Image ◽  
Pixel Size ◽  
Wavelet Method ◽  
Planar Scintigraphy ◽  
Image Enlargement ◽  
Pixel Image

The planar scintigraphic image usually has poor resolution and contains noise. This noise can be removed using the coiflet wavelet method so that the image quality gets better. This coiflet wavelet method is a noise reduction method based on frequency analysis. The planar scintigraphy image is the reconstructed image of the gamma radiation count data (phantom with the Cs-137 source in it). The original image is 15×15 pixel. Before the de-noising process, the image went through an interpolation process, which is to increase the pixel size of the image. The original image enlarged to 70×70, 480×480, and 1200×1200 pixel. After de-noising with coiflet wavelet, the image quality is measured based on MSE and PSNR parameters. The resulting images are quite good, with MSE values are close to zero and PSNR values of more than 60 dB. The smaller the MSE and the bigger the PSNR, is getting the better the image quality. In this study, the results show that the 1200×1200 pixel image has the best quality. It means that the image enlargement process has a good effect on the de-noising process, especially if the original image has a low resolution.

Blind Digital Image Watermarking Algorithm Based on the Chain Code

2012 ◽  
Vol 546-547 ◽  
pp. 410-415
Author(s):  
Chun Ge Tang ◽  
Tie Sheng Fan ◽  
Lei Liu ◽  
Zhi Hui Li
Keyword(s):  
Image Quality ◽  
Digital Watermarking ◽  
Digital Image ◽  
Image Watermarking ◽  
Boundary Representation ◽  
Experimental Results ◽  
Digital Image Watermarking ◽  
Original Image ◽  
Chain Code ◽  
Decision Method

A new blind digital watermarking algorithm based on the chain code is proposed. The chain code is obtained by the characteristics of the original image -the edge contour. The feather can reflect the overall correlation of the vector image, and chain code expression can significantly reduce the boundary representation of the amount of data required. For the watermarking embedding, the original vector image is divided into sub-block images, and two bits of the watermarking information are embedded into sub-block images repeatedly by quantization. For watermarking extracting, the majority decision method is employed to determine the size of the extracted watermark. Experimental results show that the image quality is not significantly lowered after watermarking. The algorithm can resist the basic conventional attacks and has good robustness on the shear attacks.

scholarly journals Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

2020 ◽  
Vol 7 (3) ◽  
pp. 432
Author(s):  
Windi Astuti
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Super Resolution ◽  
Linear Interpolation ◽  
Resolution Method ◽  
Image Zooming ◽  
Image Enlargement ◽  
Three Stages

Various types of image processing that can be done by computers, such as improving image quality is one of the fields that is quite popular until now. Improving the quality of an image is necessary so that someone can observe the image clearly and in detail without any disturbance. An image can experience major disturbances or errors in an image such as the image of the screenshot is used as a sample. The results of the image from the screenshot have the smallest sharpness and smoothness of the image, so to get a better image is usually done enlargement of the image. After the screenshot results are obtained then, the next process is cropping the image and the image looks like there are disturbances such as visible blur and cracked. To get an enlarged image (Zooming image) by adding new pixels or points. This is done by the super resolution method, super resolution has three stages of completion, first Registration, Interpolation, and Reconstruction. For magnification done by linear interpolation and reconstruction using a median filter for image refinement. This method is expected to be able to solve the problem of improving image quality in image enlargement applications. This study discusses that the process carried out to implement image enlargement based on the super resolution method is then built by using R2013a matlab as an editor to edit programs

Influence of scatter reduction method and monochromatic beams on image quality and dose in mammography

2003 ◽  
Vol 30 (12) ◽  
pp. 3156-3164 ◽  
Author(s):  
Raphaël Moeckli ◽  
Francis R. Verdun ◽  
Stefan Fiedler ◽  
Marc Pachoud ◽  
Shelley Bulling ◽  
...  
Keyword(s):  
Image Quality ◽  
Reduction Method ◽  
Scatter Reduction

Comparison and Simulation of Subpixel Imaging Modes for Linear CCD

2012 ◽  
Vol 236-237 ◽  
pp. 1032-1037 ◽  
Author(s):  
Ya Peng Li ◽  
Bin He ◽  
Ting Yu Liu
Keyword(s):  
Image Quality ◽  
Focal Length ◽  
Pixel Size ◽  
Nyquist Frequency ◽  
Point Sampling ◽  
Linear Ccd ◽  
Simulation Results ◽  
The Cost ◽  
Quincunx Sampling

Subpixel technique of linear CCD is effective to enhance the spatial resolution without increasing the focal length of optics and reducing the pixel size. To compare image quality of two main subpixel imaging modes, quincunx sampling and four-point sampling, a method to quantitatively evaluate image quality of subpixel based on MTF was proposed. The MTF of quincunx and four-point sampling modes were derived. Analytical results shows that theoretical limiting resolution of quincunx sampling and four-point sampling is improved to 1.4 and 1.86 times respectively, and MTF values at Nyquist frequency of two modes are increased by 0.1106 and 0.1679, respectively. MTFA in (0, 0.5) of two subpixel imaging modes were calculated and results illustrates that four-point sampling offers much more improvement with image quality than quincunx sampling, at the cost of double amount of data. A model for simulating subpixel imaging using Matlab was established, and simulation results of spoke target verify the theoretical analysis.

scholarly journals Interview with Davide Pozzi

2014 ◽  
Author(s):  
Yvonne Ng
Keyword(s):  
Image Quality ◽  
Future Generations ◽  
Controlled Environment ◽  
Film Material ◽  
Original Image ◽  
Film Studios ◽  
Film Preservation ◽  
The World ◽  
Preservation And Restoration ◽  
Digital Processes

INTERVIEW WITH DAVIDE POZZI, DIRECTOR, L'IMMAGINE RITROVATA Less often under the spotlight than filmmaking, producing or reviewing, film preservation and restoration are nevertheless crucial in keeping film cultural heritage alive for future generations. While preservation focuses on the proper storage of a film in a climate-controlled environment and may sometimes include film repair and copying, restoration aims to return a film to its entirety and original image quality as intended by its filmmakers, either through photochemical or digital processes. In recent decades, it has become increasingly clear to filmmakers, film historians, archives and libraries around the world that there is an urgent need to preserve and restore the fast growing amount of recorded film material that is rapidly being lost to deterioration. At the same time, film studios and film archives are realizing the commercial potential of restoring their collections of classics and re-releasing them theatrically as well as...

scholarly journals Penapisan Derau Gaussian, Speckle dan Salt&Pepper Pada Citra Warna

2011 ◽  
Vol 5 (3) ◽  
pp. 29
Author(s):  
Ika Purwanti Ningrum ◽  
Agfianto Eko Putra ◽  
Dian Nursantika
Keyword(s):  
Image Quality ◽  
Digital Image ◽  
Color Image ◽  
Median Filter ◽  
Bilateral Filter ◽  
Filter Method ◽  
Noise Filtering ◽  
Original Image ◽  
Average Filter

Quality of digital image can decrease becouse some noises. Noise can come from lower quality of image recorder, disturb when transmission data process and weather. Noise filtering can make image better becouse will filtering that noise from the image and can improve quality of digital image. This research have aim to improve color image quality with filtering noise. Noise (Gaussian, Speckle, Salt&Pepper) will apply to original image, noise from image will filtering use Bilateral Filter method, Median Filter method and Average Filter method so can improve color image quality. To know how well this research do, we use PSNR (Peak Signal to Noise Ratio) criteria with compared original image and filtering image (image after using noise and filtering noise).This research result with noise filtering Gaussian (variance = 0.5), highest PSNR value found in the Bilateral Filter method is 27.69. Noise filtering Speckle (variance = 0.5), highest PSNR value found in the Average Filter method is 34.12. Noise filtering Salt&Pepper (variance = 0.5), highest PSNR value found in the Median Filter method is 31.27. Keywords— Bilateral Filter, image restoration, derau Gaussian, Speckle dan Salt&Pepper

scholarly journals Compressed Sensing Reconstruction based on Adaptive Scale Parameter using Texture Feature

2019 ◽  
Vol 8 (12) ◽  
pp. 5455-5461
Keyword(s):  
Image Quality ◽  
Compressed Sensing ◽  
Scale Parameter ◽  
Sparse Matrix ◽  
Texture Feature ◽  
Projection Matrix ◽  
Original Image ◽  
Scaling Factors ◽  
Mri Scan ◽  
The Mean

While taking an MRI scan, the patients cannot static for a long time during the motions; the image formation process can create artifacts that may reduce the image quality. The Compressed Sensing (CS) mechanism is employed to reconstruct the original image from the limited data given as the sparse matrix. Hence, CS can be utilized to reduce the acceleration time for an MRI scan considering the patient's health. So the sensing method is implemented by a suitable projection matrix for reconstructing the sparse signals from a few numbers of measurements using Compressed Sensing. The CS guarantees the recovery of the original image with high probability based on random Gaussian projection matrices. However, sparse ternarius projections are more apt for the implementation of hardware. In this article, the proposed deep learning method is employed to obtain a very sparse ternary projection in Compressed Sensing. Compressed Sensing Reconstruction using an adaptive scale parameter based on the texture feature is used to improve the image quality. The two scaling factors αx and αy are assigned to specify the fixed scale for changing the improvement of the image quality. In the parameter using texture feature, the αx and αy are assigned to α as an adaptive scale based on texture feature. In the TACS-SDANN architecture, there are two layers namely the sensing layer which trains the projection matrix and a reconstruction layer which trains for non-linear sparse matrix continuously using Auto-encoder. Experimentally, the scaling factors are calculated on the training data to get the mean PeakSignal-to-Noise Ratio (PSNR) for improving the image quality. Hence a new deep network layer is employed to improve the image quality in this proposed method. Hence the consequence of the proposed method is compared with the SDANN method based on the mean Peak-Signal-to-Noise Ratio (PSNR) to check the image quality. From that comparisons, the TACS-SDANN architecture is proposed to yield a better performance.

Ахроматизированные объективы тепловизоров

2020 ◽  
Vol 14 (4) ◽  
pp. 360-367
Author(s):  
И.П. Шишкин ◽  
А.П. Шкадаревич
Keyword(s):  
Image Quality ◽  
Thermal Imaging ◽  
Infrared Region ◽  
Field Of View ◽  
Pixel Size ◽  
Variable Focus

Дано описание конструкции ахроматизированных объективов, работающих в ИК‑области спектра (8–12 мкм). При проектировании тепловизионных объективов необходимо учитывать тот факт, что размер пиксела современных микроболометров составляет 10–12 мкм, а плотность пикселей достигла формата 1 280 × 1 024. Эти параметры определяют более высокие требования, предъявляемые к качеству изображения, разрешающей способности и полю зрения разрабатываемого объектива. При этом объектив должен быть светосильным, термостабилизированным и иметь фокусировку. Рассмотрены объективы: 4-линзовый с фокусом 50 мм, 3-линзовый с фокусом 150 мм и 7-линзовый с переменным фокусом 30–150 мм. A description is given of the design of achromatized lenses operating in the infrared region of the spectrum (8–12 μm). When designing thermal imaging lenses, it is necessary to take into account the fact that the pixel size of modern microbolometers is 10–12 μm, and the pixel density has reached 1280 × 1024 format. These parameters determine higher requirements for image quality, resolution and field of view of the developed lens. In this case, the lens should be fast, thermally stabilized and have focus. The following lenses are considered: 4-lens with a focus of 50 mm, 3-lens with a focus of 150 mm and 7-lens with a variable focus of 30–150 mm.

Image Quality Assessment Based on Invariant Moments Similarity

2012 ◽  
Vol 546-547 ◽  
pp. 565-569
Author(s):  
Mei Wang ◽  
E Ye Wang ◽  
Guo Hua Pan
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Experimental Result ◽  
Original Image ◽  
Image Size ◽  
Invariant Moment ◽  
Invariant Moments ◽  
Computing Complexity ◽  
Better Than

To resolve the problems of the image quality assessment issue and the algorithm adaptability for different image size and deformation, this paper proposes a image quality assessment algorithm based on Invariant Moments Similarity. Firstly, Hu invariant moments values of original image and evaluated image are computed. Secondly the invariant moments distance is completed between original image and evaluated image. At last, the method assess the restoration image quality depend on the invariant moment distance. The experimental result shows that the algorithm result is better than MSE, PSNR, SSIM for the same-size images. And the algorithm based on invariant moment similarity can evaluate different image-size and deformation images with low computing-complexity. The assessment experimental result for difference actual images certifies the algorithm effectiveness.

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