Single-image blind deblurring for imaging sensors in the Internet of Things (IoT) is a challenging ill-conditioned inverse problem, which requires regularization techniques to stabilize the image restoration process. The purpose is to recover the underlying blur kernel and latent sharp image from only one blurred image. Under many degraded imaging conditions, the blur kernel could be considered not only spatially sparse, but also piecewise smooth with the support of a continuous curve. By taking advantage of the hybrid sparse properties of the blur kernel, a hybrid regularization method is proposed in this paper to robustly and accurately estimate the blur kernel. The effectiveness of the proposed blur kernel estimation method is enhanced by incorporating both the L 1 -norm of kernel intensity and the squared L 2 -norm of the intensity derivative. Once the accurate estimation of the blur kernel is obtained, the original blind deblurring can be simplified to the direct deconvolution of blurred images. To guarantee robust non-blind deconvolution, a variational image restoration model is presented based on the L 1 -norm data-fidelity term and the total generalized variation (TGV) regularizer of second-order. All non-smooth optimization problems related to blur kernel estimation and non-blind deconvolution are effectively handled by using the alternating direction method of multipliers (ADMM)-based numerical methods. Comprehensive experiments on both synthetic and realistic datasets have been implemented to compare the proposed method with several state-of-the-art methods. The experimental comparisons have illustrated the satisfactory imaging performance of the proposed method in terms of quantitative and qualitative evaluations.

Effective Alternating Direction Optimization Methods for Sparsity-Constrained Blind Image Deblurring

Image deblurring has been a challenging ill-posed problem in computer vision Gaussian blur is a common model for image and signal degradation The deep learning-based deblurring methods have attracted much attention due to their advantages over the traditional methods relying on hand-designed features However, the existing deep learning-based deblurring techniques still cannot perform well in restoring the fine details and reconstructing the sharp edges To address this issue, we have designed an effective end-to-end deep learning-based non-blind image deblurring algorithm In the proposed method, a multi-stream bottom-top-bottom attention network (MBANet) with the encoder-to-decoder structure is designed to integrate low-level cues and high-level semantic information, which can facilitate extracting image features more effectively and improve the computational efficiency of the network Moreover, the MBANet adopts a coarse-to-fine multi-scale strategy to process the input images to improve image deblurring performance Furthermore, the global information-based fusion and reconstruction network is proposed to fuse multi-scale output maps to improve the global spatial information and recurrently refine the output deblurred image The experiments were done on the public GoPro dataset and the realistic and dynamic scenes (REDS) dataset to evaluate the effectiveness and robustness of the proposed method The experimental results show that the proposed method generally outperforms some traditional deburring methods and deep learning-based state-of-the-art deblurring methods such as scale-recurrent network (SRN) and denoising prior driven deep neural network (DPDNN) in terms of such quantitative indexes as peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) and human vision

Image Deblurring Using Multi-Stream Bottom-Top-Bottom Attention Network and Global Information-Based Fusion and Reconstruction Network

The development of embedded systems in sustainable precision agriculture has provided an important benefit in terms of processing time and accuracy of results, which has influenced the revolution in this field of research. This paper presents a study on vegetation monitoring algorithms based on Normalized Green-Red Difference Index (NGRDI) and Visible Atmospherically Resistant Index (VARI) in agricultural areas using embedded systems. These algorithms include processing and pre-processing to increase the accuracy of sustainability monitoring. The proposed algorithm was evaluated on a real database in the Souss Massa region in Morocco. The collection of data was based on unmanned aerial vehicles images hand data using four different agricultural products. The results in terms of processing time have been implemented on several architectures: Desktop, Odroid XU4, Jetson Nano, and Raspberry. However, this paper introduces a thorough study of the Hardware/Software Co-Design approach to choose the most suitable system for our proposed algorithm that responds to the different temporal and architectural constraints. The evaluation proved that we could process 311 frames/s in the case of low resolution, which gives real-time processing for agricultural field monitoring applications. The evaluation of the proposed algorithm on several architectures has shown that the low-cost XU4 card gives the best results in terms of processing time, power consumption, and computation flexibility.

/pdf/improving-sustainable-vegetation-indices-processing-on-low-1kzrioyp.pdf

Improving Sustainable Vegetation Indices Processing on Low-Cost Architectures

A mechanical transmission based image de-blurring method used for on-line surface quality inspection

The components with micro deep holes have massive presence in different major equipment or systems. Coordinate measuring machine (CMM) remains the main equipment to measure these components because non-contact methods cannot be applied to micro deep holes. However, moving probe tips into micro holes accurately is extremely difficult for CMM through existing methods. Therefore, this study puts forward a self-calibration and high-precision vision method and system. The algorithm for automatic determination of the probe tip movement scheme is proposed, several suitable image processing methods are selected, and the vision system is designed and tested on CMM. Experimental results show that a probe tip with a diameter of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$60 \boldsymbol {\mu }\text{m}$ </tex-math></inline-formula> can be moved into micro holes below <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$100 \boldsymbol {\mu }\text{m}$ </tex-math></inline-formula> with a success rate of 100%. This method and system can be used to provide an effective means for highly precise and fast measurement of micro holes. 

Self-Calibration Vision Method and System for Automatically Moving CMM Stylus Into Micro Holes

Blind image restoration algorithms for motion blur have been deeply researched in the past years. Although great progress has been made, blurred images containing large blur and rich, small details still cannot be restored perfectly. To deal with these problems, we present a robust image restoration algorithm for motion blur of general image sensors in this paper. Firstly, we propose a self-adaptive structure extraction method based on the total variation (TV) to separate the reliable structures from textures and small details of a blurred image which may damage the kernel estimation and interim latent image restoration. Secondly, we combine the reliable structures with priors of the blur kernel, such as sparsity and continuity, by a two-step method with which noise can be removed during iterations of the estimation to improve the precision of the estimated blur kernel. Finally, we use a MR-based Wiener filter as the non-blind deconvolution algorithm to restore the final latent image. Experimental results demonstrate that our algorithm can restore large blur images with rich, small details effectively.

https://www.mdpi.com/1424-8220/16/6/845/pdf

Robust Image Restoration for Motion Blur of Image Sensors

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Robust Image Restoration for Motion Blur of Image Sensors