Image filtering is a common technique used in digital image processing that can be used to take a picture appear differently aesthetically. Noise, also known as distracting visual artifacts, can lower the overall quality of a picture, which is why image improvement techniques are required to fix the problem. It can be utilized in a variety of ways, including smoothing, sharpening, reducing noise, and detecting borders, to name a few. In this piece, we will be using convolutional techniques to correct the images that were messed up. The first thing that needs to be done is a point-by-point multiplication of the frequency domain representation of the picture that's being entered through a black image that has a small white rectangle in the mid of it. This is the first step. Only the lowest harmonics are kept after we apply a filter that gets rid of the higher ones. Because the high frequencies in the input picture are filtered out, the special domain of the image that is produced should look like a blurrier variation of the original picture. Therefore, a greater degree of detail preservation is indicated when the white rectangle W is larger because this indicates that more high-frequency components of I have been preserved.
In this paper, Framelet and Walsh transform are proposed for transformation, and then using arithmetic coding for compress an image. The goal is to achieve higher compression ratio by applying two levels Framelet transform (FLT), and then apply 2D Walsh-Hadamard transform (WHT) on each 8x8 block of the low frequency sub-band, while all other sub-bands are ignored. Experimental results show that the proposed algorithm gets best possible solution for tradeoff between compression ratio (size of image) and quality of compressed image, Peak Signal to Noise Ratio (PSNR). The simulation was carried using MATLAB software package version 2014. In this work, experiments were carried out on the gray scale and colored images
In this paper, two quantization matrices are proposed that is suitable to compress medical images using framelet transform. Also two algorithms are suggested to compress medical images. One of them is used for grayscale and color medical images while the second is used for grayscale medical images only. It is found that the first proposed quantization matrix is better than the second in terms of Peak Signal to Noise Ratio (PSNR). While the second proposed quantization matrix is faster than the first. The work suggested in this paper is compared with wavelet and multiwavelet based algorithms and other previously related works and it is found that the quantization matrices proposed are most suitable for compression medical images with framelet transform and framelet transform is the best compression method for medical images.
Removal of noise from an image is an essential part of image processing systems. In this paper a hybrid denoising algorithm which combines spatial domain Wiener filter and thresholding function in the wavelet and framelet domain is done. In this work three algorithms are proposed. The first hybrid denoising algorithm using Wiener filter with 2-level discrete wavelet transform (DWT), the second algorithm its using Wiener filter with 2-level framelet transform (FLT) and the third hybrid denoising algorithm its combines wiener filter with 1-level wavelet transform then apply framelet transform on LL of wavelet transform. The Wiener filter is applied on the low frequency subband of the decomposed noisy image. This stage will tend to cancel or at least attenuate any residual low frequency noise component. Then thresholding detail high frequency subbands using thresholding function. This approach can be used for grayscale and color images. The simulation results show that the performance of the first proposed hybrid denoising algorithm with discrete wavelet transform (db5 type) is superior to that of the second and third proposed algorithms and to that of the conventional denoising approach at most of the test noisy image with Gaussian noise and Slat & pepper noise while the third proposed denoising algorithm with hybrid wavelet & framelet transform is superior to that of the other proposed algorithms at noisy images with speckle noise.
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