Exploiting Image Local And Nonlocal Consistency For Mixed Gaussian-Impulse Noise Removal

Most existing image denoising algorithms can only deal with a single type of noise, which violates the fact that the noisy observed images in practice are often suffered from more than one type of noise during the process of acquisition and transmission. In this paper, we propose a new variational algorithm for mixed Gaussian-impulse noise removal by exploiting image local consistency and nonlocal consistency simultaneously. Specifically, the local consistency is measured by a hyper-Laplace prior, enforcing the local smoothness of images, while the nonlocal consistency is measured by three-dimensional sparsity of similar blocks, enforcing the nonlocal self-similarity of natural images. Moreover, a Split-Bregman based technique is developed to solve the above optimization problem efficiently. Extensive experiments for mixed Gaussian plus impulse noise show that significant performance improvements over the current state-of-the-art schemes have been achieved, which substantiates the effectiveness of the proposed algorithm.Comment: 6 pages, 4 figures, 3 tables, to be published at IEEE Int. Conf. on Multimedia & Expo (ICME) 201

Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation

Learning-based image super-resolution aims to reconstruct high-frequency (HF) details from the prior model trained by a set of high- and low-resolution image patches. In this paper, HF to be estimated is considered as a combination of two components: main high-frequency (MHF) and residual high-frequency (RHF), and we propose a novel image super-resolution method via dual-dictionary learning and sparse representation, which consists of the main dictionary learning and the residual dictionary learning, to recover MHF and RHF respectively. Extensive experimental results on test images validate that by employing the proposed two-layer progressive scheme, more image details can be recovered and much better results can be achieved than the state-of-the-art algorithms in terms of both PSNR and visual perception.Comment: 4 pages, 4 figures, 1 table, to be published at IEEE Int. Symposium of Circuits and Systems (ISCAS) 201

Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization

Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edges. However, the use of TV is not able to recover the fine details and textures, and often suffers from undesirable staircase artifact. To reduce these effects, this letter presents an improved TV based image CS recovery algorithm by introducing a new nonlocal regularization constraint into CS optimization problem. The nonlocal regularization is built on the well known nonlocal means (NLM) filtering and takes advantage of self-similarity in images, which helps to suppress the staircase effect and restore the fine details. Furthermore, an efficient augmented Lagrangian based algorithm is developed to solve the above combined TV and nonlocal regularization constrained problem. Experimental results demonstrate that the proposed algorithm achieves significant performance improvements over the state-of-the-art TV based algorithm in both PSNR and visual perception.Comment: 4 Pages, 1 figures, 3 tables, to be published at IEEE Int. Symposium of Circuits and Systems (ISCAS) 201

Image Restoration Using Joint Statistical Modeling in Space-Transform Domain

This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner. The main contributions are three-folds. First, from the perspective of image statistics, a joint statistical modeling (JSM) in an adaptive hybrid space-transform domain is established, which offers a powerful mechanism of combining local smoothness and nonlocal self-similarity simultaneously to ensure a more reliable and robust estimation. Second, a new form of minimization functional for solving image inverse problem is formulated using JSM under regularization-based framework. Finally, in order to make JSM tractable and robust, a new Split-Bregman based algorithm is developed to efficiently solve the above severely underdetermined inverse problem associated with theoretical proof of convergence. Extensive experiments on image inpainting, image deblurring and mixed Gaussian plus salt-and-pepper noise removal applications verify the effectiveness of the proposed algorithm.Comment: 14 pages, 18 figures, 7 Tables, to be published in IEEE Transactions on Circuits System and Video Technology (TCSVT). High resolution pdf version and Code can be found at: http://idm.pku.edu.cn/staff/zhangjian/IRJSM

Tanner Graph Based Image Interpolation

This paper interprets image interpolation as a channel decoding problem and proposes a tanner graph based interpolation framework, which regards each pixel in an image as a variable node and the local image structure around each pixel as a check node. The pixels available from low-resolution image are 'received' whereas other missing pixels of high-resolution image are 'erased', through an imaginary channel. Local image structures exhibited by the low-resolution image provide information on the joint distribution of pixels in a small neighborhood, and thus play the same role as parity symbols in the classic channel coding scenarios. We develop an efficient solution for the sum-product algorithm of belief propagation in this framework, based on a gaussian auto-regressive image model. Initial experiments show up to 3dB gain over other methods with the same image model. The proposed framework is flexible in message processing at each node and provides much room for incorporating more sophisticated image modelling techniques. ? 2010 IEEE.EI

Image interpolation via regularized local linear regression

In this paper, we present an efficient image interpolation scheme by using regularized local linear regression (RLLR). On one hand, we introduce a robust estimator of local image structure based on moving least squares, which can efficiently handle the statistical outliers compared with ordinary least squares based methods. On the other hand, motivated by recent progress on manifold based semi-supervise learning, the intrinsic manifold structure is explicitly considered by making use of both measured and unmeasured data points. In particular, the geometric structure of the marginal probability distribution induced by unmeasured samples is incorporated as an additional locality preserving constraint. The optimal model parameters can be obtained with a closed-form solution by solving a convex optimization problem. Experimental results demonstrate that our method outperform the existing methods in both objective and subjective visual quality over a wide range of test images. ? 2010 IEEE.EI

A practical algorithm for tanner graph based image interpolation

This paper interprets image interpolation as a decoding problem on tanner graph and proposes a practical belief propagation algorithm based on a gaussian autoregressive image model. This algorithm regards belief propagation as a way to generate and fuse predictions from various check nodes. A low complexity implementation of this algorithm measures and distributes the departure of current interpolation result from the image model. Convergence speed of the proposed algorithm is discussed. Experimental results show that good interpolation results can be obtained by a very small number of iterations.Engineering, Electrical & ElectronicImaging Science & Photographic TechnologyEICPCI-S(ISTP)

MoWE: Mixture of Weather Experts for Multiple Adverse Weather Removal

Currently, most adverse weather removal tasks are handled independently, such as deraining, desnowing, and dehazing. However, in autonomous driving scenarios, the type, intensity, and mixing degree of the weather are unknown, so the separated task setting cannot deal with these complex conditions well. Besides, the vision applications in autonomous driving often aim at high-level tasks, but existing weather removal methods neglect the connection between performance on perceptual tasks and signal fidelity. To this end, in upstream task, we propose a novel \textbf{Mixture of Weather Experts(MoWE)} Transformer framework to handle complex weather removal in a perception-aware fashion. We design a \textbf{Weather-aware Router} to make the experts targeted more relevant to weather types while without the need for weather type labels during inference. To handle diverse weather conditions, we propose \textbf{Multi-scale Experts} to fuse information among neighbor tokens. In downstream task, we propose a \textbf{Label-free Perception-aware Metric} to measure whether the outputs of image processing models are suitable for high level perception tasks without the demand for semantic labels. We collect a syntactic dataset \textbf{MAW-Sim} towards autonomous driving scenarios to benchmark the multiple weather removal performance of existing methods. Our MoWE achieves SOTA performance in upstream task on the proposed dataset and two public datasets, i.e. All-Weather and Rain/Fog-Cityscapes, and also have better perceptual results in downstream segmentation task compared to other methods. Our codes and datasets will be released after acceptance