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Removing NoiseImages captured using digital cameras usually have undesirable random variations in brightness or color information. This variation is often referred to as image noise [1]. Image noise is most apparent in image regions with low brightness levels, such as shadow regions or in under-exposed images. For example, the input images shown in Figures 8 and 9 have significant noise, which lowers the quality of the images. Therefore it is desirable to "clean up" some of this noise in order to increase the quality. The presence of noise in images could be due to a variety of reasons. Non-ideal camera electronics, dead pixels, and analog-to-digital converter errors are some of the common reasons. Images taken using high-end cameras have less noise because the cameras produce minimal errors. However, there is another type of noise that is more subtle, but unavoidable. This is known as the photon shot noise [1] and appears all over the image. This is due to the inherent limitation in the detection of light by image sensors. As seen in Figures 8 and 9, image noise appears as sharp variations in the image brightness. A common suggested way to digitally reduce the noise is to smooth them out. This often results in some loss of sharpness in the image. Luckily, there are noise-reduction operations that reduce noise without degrading image sharpness too much.
The Bigshot software uses a sophisticated technique to remove noise in the captured photos. However, for the sake of simplicity let us look at a simple method known as median filtering [2]. Unlike the brightness adjustment algorithms, this method works on a neighborhood of pixels. Figure 8 illustrates the idea of median filtering on a black and white image. For every pixel in the output image, we choose a small window around the corresponding pixel in the input image. The pixel values in this window are then sorted in the increasing order. Finally, the output pixel is assigned the middle value (also known as the median) of the sorted sequence. As depicted earlier in Figure 4, this process is sequentially applied for each and every pixel in the output image to generate a complete output image. The amount of noise reduced using median filtering depends on the window size. For a color image, the same operation is applied to each color separately.
The interactive demo is Figure 9 shows the effect of median filtering on a noisy image. Notice that, as you increase the size of the window, more noise is eliminated; but at the same time the image starts to appear more blurred.
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