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Sreenivasulu, P.
- Comparative Study on Removal of Salt and Pepper Noise for Various Images by Using Median Filters:A Review
Authors
1 PBR VITS, Kavali, IN
Source
Digital Image Processing, Vol 6, No 2 (2014), Pagination:Abstract
This paper exploits the removal of salt & pepper noise by using various Median filters for various test images. It gives a better idea and analysis for different Median filter algorithms for the restoration of various gray scale images that are corrupted by salt and pepper noise. Here, we are comparing the performance of different Median filters such as Standard Median Filter (MF), Adaptive Median Filter (AMF), Adaptive Weighted Algorithm (AWA), Switching Median Filter (SMF), Decision Based Algorithm (DBA), Modified Decision Based Unsymmetric Trimmed Median Filter (MDBUTMF) and their quality parameters as Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) is used to compare the performance of different Median filters for different images.Keywords
Image, Noise, Median Filter.- Wavelet based Effective Color Image Compression using Neural Networks and Modified RLC
Authors
1 Department of ECE, PBR VITS Kavali, Nellore, IN
2 Department of ECE, Jawaharlal Nehru Technological University, Hyderabad, IN
3 Department of ECE, Narayana Engineering, College, Nellore, IN
Source
Digital Image Processing, Vol 4, No 12 (2012), Pagination: 641-646Abstract
Image compression is a technique of reducing the size of image by eliminating data redundancy. It helps in reducing the amount of memory required to store an image and the time required to transmit the image over long distance. Earlier image compression is performed by using wavelet and neural network. This paper proposes a method for image compression that uses wavelet and Multilayer Feed forward neural network (MLFFN) with Error Back Propagation algorithm (EBPA), which is used to train multi layer feed forward neural network with an excellent input and output mapping. This algorithm is used for LL2 component and Modified Run Length Coding (RLC) to LH2, HL2 components with hard threshold to discard insufficient coefficients. Performance of proposed image compression method is evaluated using Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE). These estimation parameters were found to be greater when compared to image compression methods SOFM, EZW, SPIHT.