Handbook of Medical Imaging—Processing and Analysis

Due to the rapid development of artificial intelligence technology, industrial sectors are revolutionizing in automation, reliability, and robustness, thereby significantly increasing quality and productivity. Most of the surveillance and industrial sectors are monitored by visual sensor networks capturing different surrounding environment images. However, during tempestuous weather conditions, the visual quality of the images is reduced due to contaminated suspended atmospheric particles that affect the overall surveillance systems. To tackle these challenges, this article presents a computationally efficient lightweight convolutional neural network referred to as Light-DehazeNet (LD-Net) for the reconstruction of hazy images. Unlike other learning-based approaches, which separately measure the transmission map and the atmospheric light, our proposed LD-Net jointly estimates both the transmission map and the atmospheric light using a transformed atmospheric scattering model. Furthermore, a color visibility restoration method is proposed to evade the color distortion in the dehaze image. Finally, we conduct extensive experiments using synthetic and natural hazy images. The quantitative and qualitative evaluation on different benchmark hazy datasets verify the superiority of the proposed method over other state-of-the-art image dehazing techniques. Moreover, additional experimentation validates the applicability of the proposed method in the object detection tasks. Considering the lightweight architecture with minimal computational cost, the proposed system is encouraged to be incorporated as an integral part of the vision-based monitoring systems to improve the overall performance.

Light-DehazeNet: A Novel Lightweight CNN Architecture for Single Image Dehazing

Single image dehazing, which aims at restoring a haze-free image from its correspondingly unconstrained hazy scene, is a fundamental yet challenging task and has gained immense popularity recently. However, the images recovered by some existing haze-removal methods often contain haze, artifacts, and color distortions, which severely degrade the visual quality and have negative impacts on subsequent computer vision tasks. To this end, we propose a network combining multi-scale hierarchical feature fusion and mixed convolution attention to progressively and adaptively enhance the dehazing performance. The haze levels and image structure information are accurately estimated by fusing multi-scale hierarchical features, thus the model restores images with less remaining haze. The proposed mixed convolution attention mechanism is capable of reducing feature redundancy, learning compact and effective internal representations and highlighting task-relevant features, thus, it can further help the model estimate images with sharper textural details and more vivid colors. Furthermore, a deep semantic loss is also proposed to highlight essential semantic information in deep features. The experimental results show that the proposed method outperforms state-of-the-art haze removal algorithms. 

Hierarchical Feature Fusion With Mixed Convolution Attention for Single Image Dehazing

Prior guided conditional generative adversarial network for single image dehazing

Purpose: Nowadays, the number of patients with COVID-19 pneumonia worldwide is still increasing The clinical diagnosis of COVID-19 pneumonia faces challenges, such as the difficulty to perform RT-PCR tests in real time, the lack of experienced radiologists, clinical low-quality images, and the similarity of imaging features of community-acquired pneumonia and COVID-19 Therefore, we proposed an artificial intelligence model GARCD that uses chest CT images to assist in the diagnosis of COVID-19 in real time It can show better diagnostic performance even facing low-quality CT images Methods: We used 14,129 CT images from 104 patients A total of 12,929 samples were used to build artificial intelligence models, and 1200 samples were used to test its performance The image quality improvement module is based on the generative adversarial structure It improves the quality of the input image under the joint drive of feature loss and content loss The enhanced image is sent to the disease diagnosis model based on residual convolutional network It automatically extracts the semantic features of the image and then infers the probability that the sample belongs to COVID-19 The ROC curve is used to evaluate the performance of the model Results: This model can effectively enhance the low-quality image and make the image that is difficult to be recognized become recognizable The model proposed in this paper reached 97 8% AUC, 96 97% sensitivity and 91 16% specificity in an independent test set ResNet, GADCD, CNN, and DenseNet achieved 80 9%, 97 3%, 70 7% and 85 7% AUC in the same test set, respectively By comparing the performance with related works, it is proved that the model proposed has stronger clinical usability Conclusion: The method proposed can effectively assist doctors in real-time detection of suspected cases of COVID-19 pneumonia even faces unclear image It can quickly isolate patients in a targeted manner, which is of positive significance for preventing the further spread of COVID-19 pneumonia

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Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images

In this paper, a novel single image super-resolution (SR) method based on progressive-iterative approximation is proposed. To preserve textures and clear edges, the image SR reconstruction is treated as an image progressive-iterative fitting procedure and achieved by iterative interpolation. Due to different features in different regions, we first employ the nonsubsampled contourlet transform (NSCT) to divide the image into smooth regions, texture regions, and edges. Then, a hybrid interpolation scheme based on curves and surfaces is proposed, which differs from the traditional surface interpolation methods. Specifically, smooth regions are interpolated by the non-uniform rational basis spline (NURBS) surface geometric iteration. To retain textures, control points are increased, and the progressive-iterative approximation of the NURBS surface is employed to interpolate the texture regions. By considering edges in an image as curve segments that are connected by pixels with dramatic changes, we use NURBS curve progressive-iterative approximation to interpolate the edges, which sharpens the edges and can maintain the image edge structure without jaggy and block artifacts. The experimental results demonstrate that the proposed method significantly outperforms the state-of-the-art methods in terms of both subjective and objective measures.

A Single-Image Super-Resolution Method Based on Progressive-Iterative Approximation

To address the hazy image degradation problem, we introduce a single image numerical iterative dehazing method based on local physical features. The method involves three components: region division based on haze density, local atmospheric light estimation and transmission map estimation, and recovery of hazy image scene radiance by using an iterative algorithm. Because of the nonuniform haze density within an image, we first employ the affinity propagation (AP) clustering algorithm to divide a hazy image into different haze density regions. Second, to reflect the difference in atmospheric light among regions and avoid the generation of halo artifacts in recovered images, we estimate the local atmospheric light in each region to replace the global atmospheric light and then estimate the transmission via a dark channel prior. Finally, an iterative dehazing algorithm, which can be used to not only further optimize local atmospheric light and transmission but also remove haze completely, is developed based on a physical model. Experimental results illustrate that our method can effectively improve the quality of a foggy image without sacrificing color fidelity and can retain image details sufficiently.

Ping Wang

Papers

A Single-Image Super-Resolution Method Based on Progressive-Iterative Approximation

Single Image Numerical Iterative Dehazing Method Based on Local Physical Features