<![CDATA[Nowadays, topic of wavelet has many applications including image denoising. Wavelet Transform is divided into discrete wavelet transform and continuous wavelet transform. Besides for image denoising, it can also useful for image compression and others. In this research is discussed about steps image denoising using wavelet. Wavelets that used are Haar, Daubechies, biorthogonal, symlets and coiflets wavelets for hard thresholding and soft thresholding. Program is made according to steps/algorithm that were created. Then, we compare original image and result of image denoising. In this research, we use grayscale test image Lena and cat with size pixels. We use peak signal to noise ratio (PSNR) to measure performance of the algorithm. We also compare computational time.]]>
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