A hybrid algorithm for spatial and wavelet domain image restoration

The recent algorithm ForWaRD based on the two steps: (i) the Fourier domain deblurring and (ii) wavelet domain denoising, shows better restoration results than those using traditional image restoration methods. In this paper, we study other deblurring schemes in ForWaRD and demonstrate such two-step approach is effective for image restoration.published_or_final_versionS P I E Conference on Visual Communications and Image Processing 2005, Beijing, China, 12-15 July 2005. In Proceedings Of Spie - The International Society For Optical Engineering, 2005, v. 5960 n. 4, p. 59605V-1 - 59605V-

Etude comparative de méthodes de restauration d'images dans le cas d'un modèle de dégradation spatialement variant

Nous proposons une comparaison des méthodes de : Tikhonov-Miller, Tikhonov-Miller avec modèle a priori de la solution, Markov et Maximum d'Entropie, dans le cas d'un système de dégradation spatialement variant. L'approximation du modèle variant par un modèle invariant est précisée. Il est montré que le choix d'une méthode de restauration sera fonction du type d'information a priori disponible et des caractéristiques de la PSF (Point Spread Function). Dans le cas de l'imagerie par sténopé, il est indispensable de prendre en compte la variabilité spatiale

Adaptive Image Restoration: Perception Based Neural Nework Models and Algorithms.

Abstract This thesis describes research into the field of image restoration. Restoration is a process by which an image suffering some form of distortion or degradation can be recovered to its original form. Two primary concepts within this field have been investigated. The first concept is the use of a Hopfield neural network to implement the constrained least square error method of image restoration. In this thesis, the author reviews previous neural network restoration algorithms in the literature and builds on these algorithms to develop a new faster version of the Hopfield neural network algorithm for image restoration. The versatility of the neural network approach is then extended by the author to deal with the cases of spatially variant distortion and adaptive regularisation. It is found that using the Hopfield-based neural network approach, an image suffering spatially variant degradation can be accurately restored without a substantial penalty in restoration time. In addition, the adaptive regularisation restoration technique presented in this thesis is shown to produce superior results when compared to non-adaptive techniques and is particularly effective when applied to the difficult, yet important, problem of semi-blind deconvolution. The second concept investigated in this thesis, is the difficult problem of incorporating concepts involved in human visual perception into image restoration techniques. In this thesis, the author develops a novel image error measure which compares two images based on the differences between local regional statistics rather than pixel level differences. This measure more closely corresponds to the way humans perceive the differences between two images. Two restoration algorithms are developed by the author based on versions of the novel image error measure. It is shown that the algorithms which utilise this error measure have improved performance and produce visually more pleasing images in the cases of colour and grayscale images under high noise conditions. Most importantly, the perception based algorithms are shown to be extremely tolerant of faults in the restoration algorithm and hence are very robust. A number of experiments have been performed to demonstrate the performance of the various algorithms presented

From uncertainty to adaptivity : multiscale edge detection and image segmentation

This thesis presents the research on two different tasks in computer vision: edge detection and image segmentation (including texture segmentation and motion field segmentation). The central issue of this thesis is the uncertainty of the joint space-frequency image analysis, which motivates the design of the adaptive multiscale/multiresolution schemes for edge detection and image segmentation. Edge detectors capture most of the local features in an image, including the object boundaries and the details of surface textures. Apart from these edge features, the region properties of surface textures and motion fields are also important for segmenting an image into disjoint regions. The major theoretical achievements of this thesis are twofold. First, a scale parameter for the local processing of an image (e.g. edge detection) is proposed. The corresponding edge behaviour in the scale space, referred to as Bounded Diffusion, is the basis of a multiscale edge detector where the scale is adjusted adaptively according to the local noise level. Second, an adaptive multiresolution clustering scheme is proposed for texture segmentation (referred to as Texture Focusing) and motion field segmentation. In this scheme, the central regions of homogeneous textures (motion fields) are analysed using coarse resolutions so as to achieve a better estimation of the textural content (optical flow), and the border region of a texture (motion field) is analysed using fine resolutions so as to achieve a better estimation of the boundary between textures (moving objects). Both of the above two achievements are the logical consequences of the uncertainty principle. Four algorithms, including a roof edge detector, a multiscale step edge detector, a texture segmentation scheme and a motion field segmentation scheme are proposed to address various aspects of edge detection and image segmentation. These algorithms have been implemented and extensively evaluated