Structured Dictionary Learning for Image Denoising under Mixed Gaussian and Impulse Noise

Development of an Effective Corruption-Related Scenario-Based Testing Approach for Robustness Verification and Enhancement of Perception Systems in Autonomous Driving

Given that sensor-based perception systems are utilized in autonomous vehicle applications, it is essential to validate such systems to ensure their robustness before they are deployed. In this study, we propose a comprehensive simulation-based process to verify and enhance the robustness of sensor-based perception systems in relation to corruption. Firstly, we introduce a methodology and scenario-based corruption generation tool for creating a variety of simulated test scenarios. These scenarios can effectively mimic real-world traffic environments, with a focus on corruption types that are related to safety concerns. An effective corruption similarity filtering algorithm is then proposed to eliminate corruption types with high similarity and identify representative corruption types that encompass all considered corruption types. As a result, we can create efficient test scenarios for corruption-related robustness with reduced testing time and comprehensive scenario coverage. Subsequently, we conduct vulnerability analysis on object detection models to identify weaknesses and create an effective training dataset for enhancing model vulnerability. This improves the object detection models' tolerance to weather and noise-related corruptions, ultimately enhancing the robustness of the perception system. We use case studies to demonstrate the feasibility and effectiveness of the proposed procedures for verifying and enhancing robustness. Furthermore, we investigate the impact of various "similarity overlap threshold" parameter settings on scenario coverage, effectiveness, scenario complexity (size of training and testing datasets), and time costs.

A Multichannel Intraluminal Impedance Gastroesophageal Reflux Characterization Algorithm Based On Sparse Representation

Gastroesophageal reflux disease (GERD) is a common digestive disorder with troublesome symptoms that has been affected millions of people worldwide. Multichannel Intraluminal Impedance-pH (MII-pH) monitoring is a recently developed technique, which is currently considered as the gold standard for the diagnosis of GERD. In this paper, we address the problem of characterizing gastroesophageal reflux events in MII signals. A GER detection algorithm has been developed based on the sparse representation of local segments. Two dictionaries are trained using the online dictionary learning approach from the distal impedance data of selected patches of GER and no specific patterns intervals. A classifier is then designed based on the l_p-norm of dictionary approximations. Next, a preliminary permutation mask is obtained from the classification results of patches, which is then used in post-processing procedure to investigate the exact timings of GERs at all impedance sites. Our algorithm was tested on 33 MII episodes, resulting a sensitivity of 96.97% and a positive predictive value of 94.12%.

Polarization prior to single-photon counting image denoising

Single-photon counting (SPC) imaging technique, which can detect targets in extremely low light levels, has attracted considerable research interest in recent years. To reduce the influence of noise under the low light condition, traditional approaches typically seek various priors from images themselves to construct denoising models, leading to inferior performance as the signal and noise cannot be efficiently distinguished. To address this challenging problem, in this study we propose a novel polarization prior to SPC image denoising based on the observation that a special polarization SPC (PSPC) image has a higher SNR than the SPC image. It enables us to construct a polarization prior to the PSPC image that can transfer efficient targets' spatial details to the denoised SPC image, and hence improves the denoising performance. Specifically, we group similar patches of the PSPC image to form 'anti-noise' dictionaries with high SNR. Then we construct a non-local prior-oriented sparse representation constraint based on the fact that each noisy patch of the SPC image can be sparsely represented by the corresponding 'anti-noise' dictionary. According to this sparse representation constraint, we further formulate an SPC image denoising model by incorporating two terms, i.e., a negative Poisson log-likelihood function for preserving the data fidelity and a total variation constraint to reduce the influence of noise, which is solved by an efficient variable splitting method. In the experiment, we have verified the effectiveness of the proposed method from simulated and real data in terms of visual comparison and quantitative analysis, respectively.

Is large improvement in efficiency of impulsive noise removal in color images still possible?

The substantial improvement in the efficiency of switching filters, intended for the removal of impulsive noise within color images is described. Numerous noisy pixel detection and replacement techniques are evaluated, where the filtering performance for color images and subsequent results are assessed using statistical reasoning. Denoising efficiency for the applied detection and interpolation techniques are assessed when the location of corrupted pixels are identified by noisy pixel detection algorithms and also in the scenario when they are already known. The results show that improvement in objective quality measures can be achieved by using more robust detection techniques, combined with novel methods of corrupted pixel restoration. A significant increase in the image denoising performance is achieved for both pixel detection and interpolation, surpassing current filtering methods especially via the application of a convolutional network. The interpolation techniques used in the image inpainting methods also significantly increased the efficiency of impulsive noise removal.

Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images

It is essential to restore digital images corrupted by noise to make them more useful. Many approaches have been proposed to restore images affected by fixed value impulse noise, but they still do not perform well at high noise density. This paper presents a new method to improve the detection and removal of fixed value impulse noise from digital images. The proposed method consists of two stages. The first stage is the noise detection stage, where the difference values between the pixels and their surrounding pixels are computed to decide whether they are noisy pixels or not. The second stage is the image denoising stage. In this stage, the original intensity value of the noisy pixels is estimated using only their first-order and second-order neighborhood pixels. These neighboring orders are based on the Euclidean distance between the noisy pixel and its neighboring pixels. The proposed method was evaluated by comparing it with some of the recent methods using 50 images at 18 noise densities. The experimental results confirm that the proposed method outperforms the existing filters, excelling in noise removal capability with structure and edge information preservation.