درس پردازش تصویر یکی از دروس مهم در مهندسی برق و کامپیوتر می باشد که دارای پروژه های مختلف برنامه نویسی است. ارتقاء تصویر در دامنه مکانی (Image Enhancement In Spatial Domain) یکی از مباحثی که در این درس مطرح می شود. فیلترهای فرکانسی می توانند برای ارتقای تصویر مثلا برای صاف کردن، محو کردن، تیز کردن و یا پیدا کردن لبه های تصویر استفاده شود. با تضعیف مولفه های فرکانس بالای تبدیل فوریه یک تصویر، می توان عمل هموار سازی را انجام داد. از آنجایی که لبه ها و سایر تغییرات ناگهانی در شدت، به مولفه های فرکانس بالا مربوط اند، تیز کردن تصویر در حوزه فرکانس با فیلتر کردن بالا گذر انجام می شود به این صورت که مولفه های با فرکانس پایین را بدون آسیب رساندن به اطلاعات در تبدیل فوریه تضعیف نماید .
برخی از مباحثی که در این بخش ارائه و برنامه نویسی متلب آن ارائه می شود به شرح زیر است :
![](data:image/jpeg;base64,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)
- Fourier Spectrum and Average
- Low pass Filtering
- High pass Filtering Using a Low pass Image
- High pass Filtering with Ideal, Butterworth
- Correlation in the Frequency Domain
به همراه جزوه، برنامه های متلب پروژه نیز قرار داده شده است.