Minimal path techniques can efficiently extract geometrically curve-like structures by finding the path with minimal accumulated cost between two given endpoints. Though having found wide practical applications (e.g., line identification, crack detection, and vascular centerline extraction), minimal path techniques suffer from some notable problems. The first one is that they require setting two endpoints for each line to be extracted (endpoint problem). The second one is that the connection might fail when the geodesic distance between the two points is much shorter than the desirable minimal path (shortcut problem). In addition, when connecting two distant points, the minimal path connection might become inefficient as the accumulated cost increases over the propagation and results in leakage into some non-feature regions near the starting point (accumulation problem). To address these problems, this paper proposes an approach termed minimal path propagation with backtracking. We found that the information in the process of backtracking from reached points can be well utilized to overcome the above problems and improve the extraction performance. The whole algorithm is robust to parameter setting and allows a coarse setting of the starting point. Extensive experiments with both simulated and realistic data are performed to validate the performance of the proposed method.

Noise detection accuracy is crucial in suppressing random-valued impulse noise. Both false and miss detections determine the final estimation performance. Deterministic detection methods, which distinctly classify pixels into noisy or uncorrupted pixels, tend to increase the estimation error because some uncorrupted edge points are hard to discriminate from the random-valued impulse noise points. This paper proposes an iterative Structure-adaptive Fuzzy Estimation (SAFE) for random-valued impulse noise suppression. This SAFE method is developed in the framework of Gaussian Maximum Likelihood Estimation (GMLE). The structure-adaptive fuzziness is reflected by two structure-adaptive metrics based on pixel reliability and patch similarity, respectively. The reliability metric for each pixel (as noise free) is estimated via a novel Minimal Path Based Structure Propagation (MPSP) to give full consideration of the spatially varying image structures. A robust iteration stopping strategy is also proposed by evaluating the re-estimation error of the uncorrupted intensity information. Comparative experiment results show that the proposed structure-adaptive fuzziness can lead to effective restoration. Efficient implementation of this SAFE method is also realized via GPU (Graphic Processing Unit)-based parallelization.

This paper reviews the first-ever image demoireing challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ICCV 2019. This paper describes the challenge, and focuses on the proposed solutions and their results. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. A new dataset, called LCDMoire was created for this challenge, and consists of 10,200 synthetically generated image pairs (moire and clean ground truth). The challenge was divided into 2 tracks. Track 1 targeted fidelity, measuring the ability of demoire methods to obtain a moire-free image compared with the ground truth, while Track 2 examined the perceptual quality of demoire methods. The tracks had 60 and 39 registered participants, respectively. A total of eight teams competed in the final testing phase. The entries span the current the state-of-the-art in the image demoireing problem.

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compressed sensing is a widely used framework for signal reconstruction. In order to handle some practical cases in which the sparsity level is unknown, we present an improved sparsity adaptive matching pursuit (SAMP) algorithm, named variable step size stagewise adaptive matching pursuit (VSStAMP) algorithm. The proposed algorithm alternatively estimates the sparsity level and the support set of signal stage by stage. The attractive characteristic is that VSStAMP can adaptively choose the best matched estimated sparsity level by using different step sizes in different stages. The simulation results show that the stagewise adaptive matching pursuit algorithm with variable step size is feasible with higher reconstruction performance comparable with other matching pursuit algorithms.