— Image denoising is a key issue in all image processing researches. Generally, the quality of an image could be corrupted by a lot of noise due to the undesired conditions of image acquisition phase or during the transmission. Many approaches to image restoration are aimed at removing either Gaussian or impulse noise. Nevertheless, it is possible to find them operating on the same image, which...

In this paper, noise reduction scheme for images corrupted by various types of noise is presented. The efficient schemes are used to reduce the various types of noises. Images are often degraded by noise. Noise can occur during image capture, transmission, etc. Noise removing is an important mission in image processing. Generally the quality of the image processing technique has been strongly i...

Image segmentation is one of the most common steps in digital image processing. The area many image segmentation algorithms (e.g., thresholding, edge detection, and region growing) employed for classifying a digital image into different segments. In this connection, finding a suitable algorithm for medical image segmentation is a challenging task due to mainly the noise, low contrast, and steep...

Image denoising is the manipulation of the image data to produce a visually high quality image. The existing or current denoising algorithms or approaches are filtering approach, multifractal approach and wavelet based approach. Different noise models include noise as additive and multiplicative type. They include Gaussian noise, salt and pepper noise (impulsive noise), Brownian noise and speck...

The presence of noise in images can significantly impact the performances of computer vision algorithms and digital image processing. Thus, noise should be removed to improve the robustness of the entire process. Denoising or noise reduction is one of the most essential processes for digital image processing. The main goal of denoising is how to remove the noise while keeping the important feat...

Segmentation plays a key role in many image processing applications, including SAR image analysis. Given a high-quality segmentation, important tasks such as adaptive noise filtering and classification can be greatly simplified. However, the presence of a strong, multiplicative noise known as speckle makes segmentation of SAR images very difficult. A new, hybrid segmentation technique has permi...