When an Image is formed, factors such as lighting (spectra, source, and intensity) and camera characteristics (sensor response, lenses) effect the appearance of the image. So, the prime factor that reduces the quality of the image is Noise. Noise hides the important details of images. To enhance the image qualities, we have to remove noises from the images without loss of any image information. Noise removal is one of the pre-processing stages of image processing. There are different types of noises which corrupt the images. These noises are appeared on images in different ways: at the time of acquisition due to noisy sensors, due to faulty scanner or due to faulty digital camera, due to transmission channel errors, due to corrupted storage media. In order to get enhanced images, many researchers present several methods to remove noises from different types of images by preserving important details like structural features, textural information. In this paper, we present a survey on types of Noises, types of images and noise removal algorithms. We have considered three types of noises: Impulse noises, Speckle noise, Gaussian noise from two most useful images: sensor images, medical images and gray scale images. We analyze all noise removal algorithms for each noise from each of these images. At the end of our study, we present comparative study of all these algorithms and, conclude with several promising directions for future research work.

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