Image denoising has become one of the major forms of image enhancement methods that form the basis of image processing. Due to the inconsistencies in the machinery producing these signals, medical images tend to require these techniques. In real time, images do not contain a single noise, and instead they contain multiple types of noise distributions in several indistinct regions. This paper presents an image denoising method that uses Metaheuristics to perform noise identification. Adaptive block selection is used to identify and correct the noise contained in these blocks. Though the system uses a block selection scheme, modifications are performed on pixelto-pixel basis and not on the entire blocks; hence the image accuracy is preserved. PSO is used to identify the noise distribution, and appropriate noise correction techniques are applied to denoise the images. Experiments were conducted using salt and pepper noise, Gaussian noise and a combination of both the noise in the same image. It was observed that the proposed method performed effectively on noise levels up-to 0.5 and was able to produce results with PSNR values ranging from 20 to 30 in most of the cases. Excellent reduction rates were observed on salt and pepper noise and moderate reduction rates were observed on Gaussian noise. Experimental results show that our proposed system has a wide range of applicability in any domain specific image denoising scenario, such as medical imaging, mammogram etc.
Image restoration is the practice of removing or reducing the degradation. Degradation occurs due to image acquisition, out of focus, and image transfer over the internet. Image restoration tries to recover images that have been degraded. The restored image is not an original image; it is an approximation of the actual image. Image restoration is the preprocessing task done before other image processing tasks such as image segmentation, image compression, etc. This research implements a restoration algorithm using hybrid filter (HF) and fuzzy logic noise detector (FLND) for the removal of impulse noise from images. The proposed technique consists of two stages. In the first stage, we split the image into a number of windows, and each window applies the hybrid filter (Mean Filter and Adaptive Median Filter). The output from the first stage is given as an input to the second stage in which FLND generates the fuzzy rules, the rules used to classify the pixel as noisy and noise free. If the pixel is considered as noisy pixel, then it is restored by median filter.The noise-free pixels were left unchanged. Increasing the (peak-signal-to noise ratio) PSNR of an image is the foremost objective of this research work. The proposed method is evaluated on standard lena image and the PSNR value at different noise level are presented in this research paper.
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