Scale-aware decomposition of images based on patch-based filtering

다국어 초록 (Multilingual Abstract)

This dissertation presents an image decomposition algorithm based on patch-based filtering, for splitting an image into a structure layer and a texture layer. There are many applications through the decomposition because each layer can be processed respectively and appropriate manipulations are accomplished. Generally, structure layer captures coarse structure with large discontinuities and a texture layer contains fine details or proper patterns. The image decomposition is done by edge-preserving smoothing where structure layer can be obtained by applying smoothing filters to an image and then a texture layer by subtracting the filtered image from the original. The main contribution of this dissertation is to design an efficient and effective edge-preserving filter that can be adapted to various scales of images. The advantage of the proposed decomposition scheme is that it is robust to noise and can be extended to a noisy image decomposition, while conventional image decomposition methods cannot be applied to a noisy image decomposition and conventional image denoising methods are not suitable for image decomposition.

To be specific, a patch-based framework is proposed in this dissertation, which is efficient in image denoising and it is designed to smooth an image while preserving details and texture. Specifically, given a pixel, the filtering output is computed as the weighted average of neighboring pixels. For computing the weights, a set of similar patches is found at each pixel by considering patch similarities based on mean squared error (MSE) and other constraints. Then, weights between each patch and its similar patches are computed respectively. With the patch weights, all the pixels in a patch are updated at the same time while adapting to the local pixel weight. For better edge-preserving smoothing, the proposed algorithm utilizes two iterations which are performed through the same smoothing filter with different parameters. Also kernel bandwidth and the number of similar patches are tuned for multi-scale image decomposition.

The proposed decomposition can be applied to many applications, such as HDR tone mapping, detail enhancement, image denoising, and image coding, etc. In detail enhancement, the proposed smoothing filter is utilized to extract image detail and enhance it. In HDR tone mapping, a typical framework is used where the smoothing operator is replaced by the proposed one to reduce contrast range of a high dynamic range image to display it on low dynamic range devices. For image denoising, a noisy input is decomposed into structure/texture/noise and the noise layer is discarded while the texture layer is restored through the histogram matching. Also a novel coding scheme named as ``structure scalable image coding scheme'' is proposed where structure layer and salient texture layer are encoded for efficient image coding. Experimental results show that the proposed framework works well for image decomposition and it is robust to the presence of noise. Also it is verified that the proposed work can be utilized in many applications. In addition, by adopting the proposed method in decomposition of a noisy image, both image denoising and image enhancement can be achieved in the proposed framework. Furthermore, the proposed image coding method reduces compression artifact and improve the performance of image coding.

목차 (Table of Contents)

• Abstract i
• Contents iv
• List of Figures vi
• List of Tables xi
• 1 Introduction 1
• 1.1 Image decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
• 1.2 Image enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
• 1.3 Image denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
• 1.3.1 Spatial denoising . . . . . . . . . . . . . . . . . . . . . . . . . 7
• 1.3.2 Transformdomain denoising . . . . . . . . . . . . . . . . . . 9
• 1.3.3 benefits of combined image decomposition and image denoising 9
• 1.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
• 2 Related work 17
• 2.1 Image decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
• 2.1.1 Laplacian subbands . . . . . . . . . . . . . . . . . . . . . . . 17
• 2.2 Edge-preserving smoothing . . . . . . . . . . . . . . . . . . . . . . . 18
• 2.2.1 Bilateral filtering . . . . . . . . . . . . . . . . . . . . . . . . . 18
• 2.2.2 Nonlocal means filtering . . . . . . . . . . . . . . . . . . . . . 21
• 3 Scale-aware decomposition of images based on patch-based filtering 23
• 3.1 Edge-preserving smoothing via patch-based framework . . . . . . . . 23
• 3.2 Multi-scale image decomposition . . . . . . . . . . . . . . . . . . . . 26
• 4 Applications 31
• 4.1 Image enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
• 4.1.1 Detail enhancement . . . . . . . . . . . . . . . . . . . . . . . 31
• 4.1.2 HDR tone mapping . . . . . . . . . . . . . . . . . . . . . . . 36
• 4.2 Image denoising . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
• 4.2.1 A noisy image decomposition . . . . . . . . . . . . . . . . . . 40
• 4.2.2 texture enhancement via histogram preservation . . . . . . . 41
• 4.2.3 image denoising via subband BLF . . . . . . . . . . . . . . . 44
• 4.2.4 Experimental results of image denoising . . . . . . . . . . . . 48
• 4.3 Image coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
• 4.3.1 Structure scalable image coding framework . . . . . . . . . . 61
• 5 Conclusion 73
• Bibliography 76