Research In Digital Image Processing Research Articles

Remove Blur Image Using Bi-Directional Akamatsu Transform and Discrete Wavelet Transform

Purpose: Image is an imitation of everything that can be materialized, and digital images are taken using a machine. Although digital image capture uses machines, digital images are not free from interference. Image restoration is needed to restore the quality of the damaged image.Methods: Bi-directional Akamatsu Transform is proven to have an effective performance in reducing blur in images. Meanwhile, Discrete Wavelet Transform has been widely used in digital image processing research. We had been investigated the image restoration method by combining Bi-directional Akamatsu Transform and Discrete Wavelet Transform. Bi-directional Akamatsu Transform applied in Low-Low (LL) sub-band is the Discrete Wavelet Transform decomposition image most similar to the original image before decomposing. In this study, there are still shortcomings, including the determination of the values of N, up_enh, and down_enh, which are still manual. Manually setting the three values makes the Bi-directional Akamatsu Transform method not get the best results. With the use of machine learning methods can get better restoration results. Further testing is also needed for a more diverse and robust blur. The image data has a resolution of 256x256, 512x512, and 1024x1024. The image will be directly converted to a grey-scale image. The converted image will be given an attack model: average blur, gaussian blur, and motion blur. The image that has been attacked will apply two restoration methods: the proposed method and the Bi-direction Akatamatsu Transform. These two restoration images will then be compared using PSNR.Result: The average PSNR value from the restoration of the proposed method is 0.1446 higher than the average PSNR value from the restoration of the Bi-directional Akamatsu Transform method. When we compare it with the average PSNR value of the Akamatsu Transform restoration method, the average PSNR of the proposed method is 0.2084.Value: The combination of DWT and akamatsu transform results produce good PSNR values even though they have gone through the blurring method in image restoration.

Multimodal Magnetic Resonance Image Brain Tumor Segmentation Based on ACU-Net Network

Medical image segmentation has the significance of research in digital image processing. It can locate and identify the organ cells, which is essential for clinical analysis, diagnosis, and treatment. Since the high heterogeneity of pathological tissues and the inconspicuous resolution in multimodal magnetic resonance images, we propose a multimodal brain tumor image segmentation method based on ACU-Net network. In the beginning, we preprocess brain images to ensure the balanced number of categories. We adopt deep separable convolutional layers to replace the ordinary architecture in the U-Net to distinguish the spatial correlation and appearance correlation of the mapped convolutional channel. We introduce residual skip connection into the ACU-Net to heighten the propagation capacity of features and quicken the convergence speed of the network, to realize the capture of deep abnormal regions. We use the active contour model to against the image noise and edge cracks, come true the tracking of tumor deformation and solve the problem of edge blur in edema area, so as to divide the tumor core and enhanced necrotic parenchymal area exactly in the abnormal area. In this paper,17926 MRI images of 335 patients in the BraTS 2015, BraTS 2018, and BraTS 2019 datasets are used for training and verifying. Our experiments demonstrate that ACU-Net network has better performance than the other segmentation algorithms in subjective vision and objective indicators when applied to brain tumor image segmentation.

A Novel Edge Detection Technique for Image Classification and Analysis

The main aim of this project is to propose a new method for image segmentation. Image is concerned with splitting an image up into segments (also called regions or areas) that each holds some property distinct from their neighbor. Simply, another word for the Object Detection is Segmentation . is divided into two types they are Supervised and Unsupervised Segmentation. consists of three types of methods which are divided on the basis of threshold, edge and region. Thresholding is a commonly used enhancement whose goal is to segment an image into object and background. Edge-based segmentations rely on edges found in an image by edge detecting operators. Region based segmentations basic idea is to divide an image into zones of maximum homogeneity, where homogeneity is an important property of regions. Edge detection has been a field of fundamental importance in digital image processing research. Edge can be defined as a pixels located at points where abrupt changes in gray level take place in this paper one novel approach for edge detection in gray scale images, which is based on diagonal pixels in 2*2 region of the image, is proposed. This method first uses a threshold value to segment the image and binary image. And then the proposed edge detector. In order to validate the results, seven different kinds of test images are considered to examine the versatility of the proposed edge detector. It has been observed that the proposed edge detector works effectively for different gray scale digital images. The results of this study are quite promising. In this project we proposed a new algorithm for edge Detection. The main advantage of this algorithm is with running mask on the original image we can detect the edges in the images by using the proposed scheme for edge detection.