PLANAR SCINTIGRAPHY IMAGE DE-NOISING USING COIFLET WAVELET

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.

Single- and Multi-Step Image Enlargement for Medical Image Coding

Background: Some interpolators cannot be used in an image magnification problem in a freely scalable form. For instance, when we want to magnify an image to a 16-time bigger scale, some interpolators have to do this process in two steps including two 4-time magnification steps, however, some are able to do it directly. Materials and Methods: For generating data of this study, MATLAB as a simulator has been used. Bi-; Linear (BL) and Cubic Convolution (CC) interpolators are the two applied re-samplers in the reconstruction of digital images. Results: Data shows that the performance of both free-size interpolators (BL and CC) is obviously different in both direct and indirect pixel reconstruction. Conclusion: The acquired data shows a less error in the condition of direct interpolation. The relative results of experiments are different from the type of core interpolators (BL and CC).

Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

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

Approximation by the Third-Order Splines on Uniform and Non-uniform Grids and Image Processing

This work is one of a series of papers that is devoted to the further investigation of polynomial splines and trigonometric splines of the third order approximation. Polynomial basis splines are better known and therefore more commonly used. However, the use of trigonometric basis splines often provides a smaller approximation error. In some cases, the use of the trigonometric approximations is preferable to the polynomial approximations. Here we continue to compare these two types of approximation. The Lebesgue functions and constants are discussed for the polynomial splines and the trigonometric splines. The examples of the applications of the splines to image enlargement are given.

Implementasi Interpolasi Fractal Untuk Pembesaran Skala Pada Citra Screen Capture CCTV

Various types of image processing that can be done by computers today such as image enlargement, image quality improvement and sharpening of an image are one of the fields that are quite popular today. Sharpening an image is very necessary so that someone can see and observe clearly the object of the image and the absence of interference. An image can have damage to some parts or blocks of pixels in the sending or storage process. This damage is the main form of error in the image, for example the image of CCTV Capture. The problem that is often faced when we want to see a CCTV recording is the obscurity of an object image that we want to see. If the Screen Capture and zoom in will be increasingly unclear the image object. The results of this Screen Capture image have a better level of sharpness if it is enlarged directly, the Screen Capture is usually used as evidence and so on. This image improvement is done by the operation process of the selection point of the results of the CCTV Screen Capture image that is used as a sample. After the Screen Capture image selection results are obtained then the image is in Grayscale to make a uniform pixel value, the next process is the process of enlarging the image of the Screen Capture image by adding new pixels. This is done by inserting the new pixel points between the points where the pixel value has been set directly from the original image. To overcome these problems requires the Fractal Interpolation method to improve image quality enhancement. With this method it is expected to be able to solve the problem of improving the image quality enlargement of the results of the CCTV Capture Screen. This research was conducted to implement the process of improving image quality magnification based on the Fractal Interpolation method.