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Natural images inevitably suffer from spatially variant blur caused by the relative motion between a camera and objects. We present an effective and efficient patch-wise edge-enhanced image regularization and a robust kernel similarity constraint to perform an accurate kernel estimation from coarse-to-fine iterations. The proposed adaptive regularization introduces a gradient magnitude penalty function into total variation to preserve and enhance salient edges while smoothing out harmful subtle structures. In addition, the similarity constraint is engaged in each patch without camera rotation effects, ensuring that the erroneous kernels can be identified by measuring the similarity among the kernels of neighbor patches and be replaced with the well-estimated ones. After obtaining accurate kernels, numerous nonblind deblurring methods can be applied to restore an image. Numerical experiments demonstrate that the proposed algorithm performs favorably without ringing artifacts and possesses high processing efficiency for natural nonuniform blurred images.

In contrast with traditional extended depth-of-field approaches, we propose a depth-based deconvolution technique that realizes the depth-variant nature of the point spread function of an ordinary fixed-focus camera. The developed technique brings a single blurred image to focus at different depth planes which can be stitched together based on a depth map to output a full-focus image. Strategies to suppress the deconvolution’s ringing artifacts are implemented on three levels: block tiling to eliminate boundary artifacts, reference maps to reduce ringing initiated by sharp edges, and depth-based masking to mitigate artifacts raised by neighboring depth-transition surfaces. The performance is validated numerically for planar and multidepth objects.

To solve the image deblurring problem, most of the recently proposed nonblind deblurring approaches focus on regularizing the solutions by adding image priors, such as sparse prior, piece-wise smooth prior (total-variation prior), and so on. This paper, however, presents a new term, which can be viewed as a supplement to the data fidelity term, to improve the nonblind deblurring result. The new term is based on the relative error in frequency domain (REFD) and can suppress the ringing artifacts and preserve the image details. We analyze the properties of REFD for image deconvolution; then, a nonblind image deconvolution algorithm is proposed via introducing the REFD term into the image deconvolution model. The experimental results demonstrate the performance and efficacy of our proposed algorithm when compared to some other state-of-the-art algorithms.

Considering statistical characteristics of multiwavelet coefficient vectors, an image fusion method with a window-based salience measure for vector fusion is proposed in the multiwavelet domain. After analyzing joint probability distribution of multiwavelet coefficient vectors, we respect the joint probability distribution as a peaky and heavy tailed two-dimensional non-Gaussian distribution, and hence model it by using a bivariate symmetric alpha-stable ( BiSαS ) distribution. Model parameters of the BiS α S distribution can be estimated from samples of multiwavelet coefficient vectors. In a sliding window, the estimated parameters can reveal the impulsive shape of the histogram of the multiwavelet coefficient vectors within the window. So we employ these model parameters to form the new salience measure indicating the degree of prominent information. In our method, vector fusion rule is introduced to avoid inconsistency of decision maps between the component sub-bands and, consequently, to lessen ringing artifacts in final fusion results. Experimental results support the effectiveness of our proposed method in the area of visual quality and objective evaluations.

The dictionary-based super-resolution (SR) method has received much attention in recent years because sparse representation is very effective for image restoration tasks. By sparse representation, original image patches are represented as a sparse linear combination of atoms in an over-complete dictionary. However, the dictionary-based SR approach has some disadvantages in that it produces annoying ringing artifacts, especially along the object boundaries and is not effective in reconstructing images that contain patterns with strong edges. We enhance the dictionary-based SR using nonlocal total variation regularization. In the training stage, we jointly train two dictionaries, Dh and D1, from the low-resolution (LR) and high-resolution (HR) training image patches by using the K-singular value decomposition (KSVD) algorithm as in conventional methods. In the synthesis stage, we obtain the sparse coefficient vector from the LR test image over the LR dictionary, and reconstruct SR patches using the coefficient vector over the HR dictionary. Then, we employ nonlocal total variation regularization to remove annoying ringing artifacts and recover the patterns with strong edges in images. Experimental results on various test images demonstrate that the proposed method is very effective in enhancing the dictionary-based SR approaches in terms of quantitative performance and visual quality.

This paper describes a new anisotropic diffusion method for removing ringing artifacts from deconvolution images. The characteristics of the deconvolution image ringing artifact are also analyzed. Unlike the previous anisotropic diffusion methods that use the global statistical value as the scale function, the proposed anisotropic diffusion defines a new scale function based on image local statistical characteristics to recognize the ringing artifacts. The proposed anisotropic diffusion performs well in terms of the images' detail preservation, error reduction, and improvement in visual quality in the presence of various levels of image ringing artifacts.

Linear space-invariant image restoration algorithms often introduce ringing effects near sharp intensity transitions. A ringing metric to evaluate the quality of images restored using iterative deconvolution algorithms is presented. According to the types of ringing artifacts, two ringing metrics are used to assess the restored images based on the analysis of ringing artifacts. An overall ringing metric is presented based on the two ringing metrics. The experimental results validate that the proposed method performs well over a wide range of restoration image ringing level assessments. Consequently, the proposed model has a good agreement with the ratings from an observer in subjective experiments.

Blind superresolution (BSR) is one of the challenges in image superresolution. We propose a new approach using a unified regularization framework, which solves image registration, point spread function (PSF) estimation, and high-resolution (HR) image reconstruction simultaneously. To achieve this, the anisotropic diffusion techniques are employed as one regularization term to preserve edge information in the HR image estimation, and a generalized version of the eigenvector-based (EVAM) constraint is developed to regularize the PSF. An alternating minimization algorithm is devised to find optimal solutions, and an effective numerical implementation scheme, based on local filtering, is proposed to suppress the ringing artifacts in the image reconstruction. Finally, experiments with synthetic and real data are presented to demonstrate the effectiveness and robustness of our approach, which can handle motion blur well and enhance resolution notably for very noisy images.

JPEG2000 Standard (ISO/IEC 15444, ITU-T T.800) is a new-generation image compression technique, enabling encoding images at low bit rates with acceptable quality. Since JPEG2000 is based on wavelet transforms, the reconstructed image will contain perceivable ringing artifacts in medium- and low-bit-rate regimes of lossy compression. We utilize a quad-tree partitioning scheme for postprocessing the reconstructed image in a spatially varying man- ner and presents a voting strategy to determine a set of morphologi- cal filters to be used for reducing the ringing artifacts. All this pro- cessing is performed at the encoder side, and the set of selected filters is conveyed to the decoder in the form of side information. Specifically, the adopted (eight) morphological filters are generated through the use of four predefined structuring elements (SEs) in conjunction with two morphological operations, namely, dilation and erosion. A voting strategy is used to select the morphological filter for each partition block to optimize the postprocessed image quality—through minimizing the sum of absolute differences under the constraint of a fixed quad-tree partition. Applying the chosen morphological filters to their corresponding blocks typically en- hances the reconstruction quality—in the worst case, leaving it un- changed. Simulation results demonstrate that the proposed tech- nique enhances reconstructed image quality compared to unprocessed JPEG2000 output at an equivalent bit rate, accounting for the side information overhead, in terms of both peak SNR (PSNR) and visible ringing measure (VRM). © 2005 SPIE and

Superresolution image reconstruction produces a high-resolution image from a set of shifted, blurred, and decimated versions thereof. Previously published techniques perform well only for small magnifications but get worse either in computational complexity or ringing artifacts for large magnifications. We propose a hybrid algorithm to reduce both ringing artifacts and computational complexity of maximum a posteriori (MAP)-based superresolution algorithms. The proposed algorithm magnifies the low-resolution image in stages, and applies a new edge-adaptive postprocessing algorithm at early stages. Experiment results show that the new approach is more efficient and can provide much better reconstruction quality in comparison with normal MAP algorithms.

We describe a novel medical image coder that adopts a hybrid wavelet deringing algorithm. The proposed algorithm uses spectrum reorganization, classification, block similarity, and a deringing filter to encode medical images. The coefficients of each wavelet tree are organized into a wavelet block to reduce image complexity. Following the spatial location of the block-gathering operation, classification and block similarity are used to further reduce the total bit rate. Meanwhile, quad-tree decomposition and a set of morphological filters for reducing the ringing artifacts is presented. This set of filters employs 8 predefined morphological operations, namely, 4 structuring elements (SEs), each of which includes both a dilation and an erosion operation. The voting strategy is used to select the most suitable morphological filter for each block. Experimental results demonstrate that the proposed technique enhances the quality of the reconstructed image in both the peak SNR (PSNR) and perceptual results compared to JPEG2000 and set partitioning in hierarchical trees (SPIHT) given the same bit rate.

Previous work has demonstrated the potential for adaptive filtration in processing digital chest images. The technique uses the histogram of the image to determine the pixels (and regions) in which edge enhancement is applied. This paper extends that work by investigating the choice of parameters used in selectively enhancing the mediastinum. The image is separated into its low and high frequency components by convolution with a square kernel. The effect of kernel size was studied with a choice of 17 x 17 mm, which was found to be sufficient to include the frequencies of interest. A serious deficiency in previous implementations of this technique is the existence of ringing artifacts at the juncture of the lung and mediastinum. These result in part from the use of a step function to specify the low frequency image intensity above which high frequencies are amplified. By replacing this step with a smoother (cosine) function, the artifact can be removed. Finally, the amplification constant was examined in light of its effect on both structure and noise in the image.