A Comparative Study Of Noise Removal Methods For Image Enhancement (Record no. 12158)
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| -- | Unimy |
| -- | eng |
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| -- | BCS 012018 02 |
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| -- | Hanan Zainal Abidin |
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| -- | A Comparative Study Of Noise Removal Methods For Image Enhancement |
| -- | / Hanan Binti Zainal Abidin |
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| -- | 2018 |
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| -- | Abstract in English |
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| -- | "A report project submitted in partial fulfillment of the requirements for the award of Bachelor of Computer Science (Hons)." -- On t. p. |
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| -- | Project paper (Bachelor of Computer Science ) - University Malaysia of Computer Science and Engineering, 2018. |
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| -- | The influence and impact of digital image processing on modern society is tremendous, and image processing is now a critical component in science and technology (Dougherty, 2009). Nowadays, this field plays as important application in recognition character for signature verification, automation target recognition or object segmentation, face and fingertip detection and medical application. The biggest concerned in this field is to enhance the image. This research defined the enhanced image as such the image is smoothed and the edge is preserved. Observed images are often corrupted with noises. This will increase the difficulty to interpret the image. There are many type of noises discussed by other image analysts. However, it is not easy to remove the noise without prior knowledge of noise model (Boyat & Joshi, 2015). This research concerned on gray scale image, where Salt and Pepper noise and Gaussian noise are two type of noises that commonly found in gray scale images. So, it is important for an analyst to study these noise models. For Salt and Pepper noise, a proposed filtering method from (Khan et al, 2010) resulted a promising output apart of the known filtering method, Median filter. So, this paper discusses the structure of the method and analyses it for Gaussian noise. From the analyzation, this research made an improvement on Khan’s method. The results discussed in term of measuring the quality of the image using mean square error (MSE) and peak signal to noise ratio (PSNR). |
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| -- | 12158 |
| -- | 12158 |
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| -- | lcc |
| -- | SC |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Collection code | Home library | Current library | Shelving location | Date acquired | Full call number | Barcode | Date last seen | Price effective from | Koha item type |
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| UNIMY | UNIMY | PJ Library | 09/27/2018 | BCS 012018 02 | 102371 | 09/27/2018 | 09/27/2018 | Special Collection |