Doctor of Philosophy (Ph.D.), University of Dayton, 2019, Electrical and Computer Engineering

Noise is present in all images captured by real-world image sensors. The distribution of real camera sensor data is well approximated by Poisson, and the estimation of the light intensity signal from the Poisson count data plays a prominent role in digital imaging. Multi-scale Poisson image denoising techniques have processed Haar frame and wavelet coefficients---being enabled by Skellam distribution analysis. Previous work has solved the minimum risk shrinkage operator (MRSO) that produces denoised wavelet coefficients with best achievable Mean Squared Error (MSE) for gray scale image. We extend the idea of MRSO to denoise color sensor data in color-opponent space, improving the quality of denoised color images. In addition, the stable representation of color is to use ratios which we denote by chromaticities. Thus we propose a new Bayes estimator for color image denoising in log-chromaticity coordinate. Using full resolution real R/G/B camera images, we verified that the proposed denoising is more stable than the state-of-art color denoising techniques, yielding higher image quality result. Furthermore, the noise parameters that characterize the level of noise in an image or video frame are required for effective denoising. We develop a novel technique to estimate the noise parameters from natural scenes by exploiting the global joint statistics across multiple video frames, which can be interpreted as a binomial random variable that is insensitive to textures and scene contents. We verify experimentally that the proposed noise parameter estimation method recovers noise parameters more accurately than the state-of-art noise parameter estimation techniques.

Committee: Keigo Hirakawa (Advisor); Russell Hardie (Committee Member); Raul Ordonez (Committee Member); Ryan Kappedal (Committee Member) Subjects: Electrical Engineering

Doctor of Philosophy, The Ohio State University, 2004, Electrical Engineering

Wavelet-based image and video compression techniques have become popular areas in the research community. In March of 2000, the Joint Pictures Expert Group (JPEG) released JPEG2000. JPEG2000 is a wavelet-based image compression standard and predicted to completely replace the original JPEG standard. In the video compression field, a compression technique called 3D wavelet compression shows promise. Thus, wavelet-based compression techniques have received more attention from the research community. This dissertation involves further investigation of the wavelet transform in the compression of image and video signals, and a rate control method for real-time transfer of wavelet-based compressed video. A pre-processing algorithm based on the wavelet transform is developed for the removal of noise in images prior to compression. The intelligent removal of noise reduces the entropy of the original signal, aiding in compressibility. The proposed wavelet-based denoising method shows a computational speedup of at least an order of magnitude than previously established image denoising methods and a higher peak signal-to-noise ratio (PSNR). A video denoising algorithm is also included which eliminates both intra- and inter-frame noise. The inter-frame noise removal technique estimates the amount of motion in the image sequence. Using motion and noise level estimates, a video denoising technique is established which is robust to various levels of noise corruption and various levels of motion. A virtual-object video compression method is included. Object-based compression methods have come to the forefront of the research community with the adoption of the MPEG-4 (Motion Pictures Expert Group) standard. Object-based compression methods promise higher compression ratios without further cost in reconstructed quality. Results show that virtual-object compression outperforms 3D wavelet compression with an increase in compression ratio and higher PSNR. Finally, a rate-control method (open full item for complete abstract)

Committee: Yuan Zheng (Advisor) Subjects: