Computer Sciences and Information Technology - Essay Example

The paper "Computer Sciences and Information Technology" is a wonderful example of an assignment on information technology. Exercise A From the above, image is the original image, 2 is a Fourier transform of the image while 3 is a centered Fourier transmute of image. Apparently, the original image has a low frequency in the corners while the frequency in the center is remarkably high. Additionally, it has a sharp edge in the middle indicating how sharp straight lines can be utilized in the production of remarkable images.

Noteworthy, the Fourier transform of image 2 has a high frequency at the edges and lighter angles connoting low frequency. Furthermore, image 1 and 3 are opposite of each other. The centered Fourier image 3 is an inversion of the image; has a high frequency in the corners and light soft edges in its central line. After eliminating the logarithmic conversion, it is noted that images 1a and 3a are quite similar to images 1 and 3. However, in image 2, there is no comparison that can be made since there are no details in the image that is produced.

  Exercise B In the above images, image (a) is the original image, image (b) is the convert of image (a) while image (c) is the reverse convert of image (b). In the image (a), there is Fourier transformation. Therefore, to rebuild the original image whose pixels and brightness are significantly similar to those in the original image, an inverse transform was indispensable. Additionally, as evident in an image (c), converse Fourier transformation can be used to reconstruct the original image.

Remarkably, a transformation of image (b) results to a dot in the center of the image which implies that there is a very high frequency at the center of the image. Likewise, images A2, B2 and C2 further elucidate and designate the results of the current exercise. Exercise C The above images indicate some noteworthy findings. Image C1 is the image of the circle with jumbled noise, C3 is the image of the circle with coherent noise while C2 and C4 are their respective transformations. Image (C1) indicate how randomly the noise is spread over the image while image (C2) clearly signpost that the noises are not absolutely jettisoned even after filtering the image.

In the image with coherent noise, it has sharp edges and the noise is systematic in the upright direction as evident in C3. In the transformed image, C4, lines disappear and soft noises form circles around the dot in the inside of the image. The circles of noises vanish as one move towards the edges and the image becomes clear. It is also imperative to note that image C2 is quite sharp as compared to image C4. It is, therefore, evident that if the noise is regular, the Fourier separates it from the image.

Nonetheless, when the noise is random, the Fourier eliminates it from the image. Exercise D These images further indicate the effects of filtering and image transformation. Image (a) is the ideal lowpass filter, (b) is the filtered spectrum of the true image with jumbled noise while image (c) is plainly the reverse convert of image (b). After applying the lowpass filter on the true image, image (b) becomes clear and better than the original image. It becomes candid and bright thus displaying finer details as compared to the original image.

Conclusively, the lowpass filter is quite useful in excluding the noise from an image so that the final image becomes clear, more visible and brighter enough to reveal more details of the original image. Exercise E In exercise E, image (a) is the Butterworth filter, the filtered spectrum of the true circle image with jumbled noise is an image (b). Image (c) is the opposite convert of image (b). Image (A), Butterworth filter, has a high frequency at the center but this frequency diminishes as it approaches the outside.

Furthermore, the spectrum amplitude of image (B) declines towards the corners of the image. However, the filtered spectrum, image (B), is clearer than the original image thus revealing more details. Its frequency is high at the center. Additionally, the amount of noise in the final image is low as compared to the original image and more details are revealed. As clearly indicated above, the Butterworth filter is convenient in excluding the noise from an image thus making the final image more clear and visible to reveal finer details of the image.