Noise Reduction of Image and Video Signals.

摘要

The thesis investigates techniques to reduce noise in image and video signals. The investigation can be divided into four parts. The first part studies methods to improve the objective and subjective quality of coded images. The proposed two efficient image postprocessing techniques are presented. The second part investigates noise reduction of image signals corrupted by Gaussian noise and presents a denoising method based on Bayes estimator. The third part investigates noise reduction of coded videos by two different approaches. The approaches are used as video loop and postprocessing filters, in order to improve the coding performance of video signals. Finally, the fourth part investigates a new edge detection method that can provide an accurate detection of edges. The method can be used together with edge-based image processing techniques to improve objective and subjective quality of noisy images.;In the first part, the technique to improve the subjective and objective quality of coded images are studied. Coding artifacts exist in images using block-based discrete cosine transform (DCT) compression standards. In order to reduce image coding artifacts, a non-local Kuan’s (NLK) filter is proposed from the minimum mean-square-error (MMSE) criterion. It is used to restore quantized DCT coefficients. Then we propose the dual non-local Kuan’s (DNLK) filter by applying the NLK filter in dual-layer. The DNLK filter is further extended to form the overcomplete dual non-local Kuan’s (OCDNLK) filter by applying to the overcomplete DCT coefficients. Experimental results on coded images using test quantization tables and JPEG coded images show the effectiveness of the two methods.;In the second part, we use a Bayes least square estimator to estimate the original transform coefficients optimally, from the Bayes perspective. It is an improved non-local Kuan’s filter by considering non-Gaussian property of transform coefficients using Gaussian scale mixture model. The residual coefficients after subtracting non-local estimated means are not Gaussian distributed, so a Gaussian scale mixture model is employed to represent the residual coefficients. Experiments demonstrate its efficiency on image denoising and better performance than the NLK filter.;In the third part, the proposed methods based on the NLK filter to improve video coding performance are studied. Direct application of the NLK filter on videos coded using intra/inter-frame prediction and transform coding cannot improve coding performance efficiently. We identify the causes of the problem and propose quadtree-based NLK (QNLK) loop filter and quadtree-based overcomplete NLK (QOCNLK) loop filter to solve the problem. NLK and overcomplete NLK loop filters are used to restore quantized residual transform coefficients. Restored coefficients are then projected onto designed quantization constraint sets (QCS). Quadtree-based signaling strategy is used for adaptive filtering control. Experimental results show that the proposed loop filtering techniques achieve significant bit rate saving and visual quality improvement compared with H.264 or advanced video coding (AVC) High Profile. We also provide the experimental results and analysis of postprocessing application using QNLK and QOCNLK filters.;In the fourth part, a new edge detecting technique using 3-D Hidden Markov Model (HMM) based on the non-decimated wavelet is studied. The proposed model can not only capture the relationship of wavelet coefficients inter-scale, but also consider the intra-scale dependence. A computationally efficient maximum likelihood (ML) estimation algorithm is employed to compute parameters and the hidden state of each coefficient is revealed by maximum a posteriori (MAP) estimation. Experimental results of natural images are provided to evaluate the algorithm. For noisy images, the method can extract edges and remove noise simultaneously. The method can be used together with edge-based image processing techniques to improve subjective and objective quality of noisy images. In addition, the proposed model has the potential to be an efficient multi-scale statistical modeling tool for other image or video processing tasks.