First Advisor

Fu Li

Date of Publication


Document Type


Degree Name

Master of Science (M.S.) in Electrical and Computer Engineering


Electrical and Computer Engineering




Photography -- Digital techniques, Image processing -- Digital techniques, Computer graphics



Physical Description

1 online resource (v, 63 p.) : ill. (some col.) + 1 mp3 file


An abstract of the thesis of Ben Huang for the Master of Science in Electric and Computer Science presented [August 12nd, 2010]. Title: Removing textured artifacts from digital photos by using spatial frequency filtering Virtually all image processing is now done with digital images. These images, captured with digital cameras, can be readily processed with various types of editing software to serve a multitude of personal and commercial purposes. But not all images are directly captured and even of those that are directly captured many are not of sufficiently high quality. Digital images are also acquired by scanning old paper images. The result is often a digital image of poor quality. Textured artifacts on some old paper pictures were designed to help protect pictures from discoloration. However, after scanning, these textured artifacts exhibit annoying textured noise in the digital image, highly degrading the visual definition of images on electronic screens. This kind of image noise is academically called global periodic noise. It is in a spurious and repetitive pattern that exists consistently throughout the image. There does not appear to be any commercial graphic software with a tool box to directly resolve this global periodic noise. Even Photoshop, considered to be the most powerful and authoritative graphic software, does not have an effective function to reduce textured noise. This thesis addresses this problem by proposing the use of an alternative graphic filter to what is currently available. To achieve the best image quality in photographic editing, spatial frequency domain filtering is utilized instead of spatial domain filtering. In frequency domain images, the consistent periodicity of the textured noise leads to well defined spikes in the frequency transform of the noisy image. When the noise spikes are at a sufficient distance from the image spectrum, they can be removed by reducing their frequency amplitudes. The filtered spectrum may then yield a noise reduced image through inverse frequency transforming. This thesis proposes a method to reduce periodic noise in the spatial frequency domain; summarizes the difference between DFT and DCT, FFT and fast DCT in image processing applications; uses fast DCT as the frequency transform to solve the problem in order to improve both computational load and filtered image quality; and develops software that can be implemented as a plug in for large graphic software to remove textured artifacts from digital images.


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Portland State University. Dept. of Electrical and Computer Engineering

Persistent Identifier