Mohammed Albekairi¹, Radhia Khdhir^2,*, Amina Magdich³, Somia Asklany^4,*, Ghulam Abbas⁵, Amr Yousef ^6,7

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.3, pp. 3607-3644, 2025, DOI:10.32604/cmes.2025.069681 - 30 September 2025

Abstract License plate recognition in haze-affected images is challenging due to feature distortions such as blurring and elongation, which lead to pixel displacements. This article introduces a Displacement Region Recognition Method (DR2M) to address such a problem. This method operates on displaced features compared to the training input observed throughout definite time frames. The technique focuses on detecting features that remain relatively stable under haze, using a frame-based analysis to isolate edges minimally affected by visual noise. The edge detection failures are identified using a bilateral neural network through displaced feature training. The training converges bilaterally… More >

Gaurav Saxena¹, Kiran Napte², Neeraj Kumar Shukla^3,4, Sushma Parihar^5,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2307-2323, 2025, DOI:10.32604/cmes.2025.067530 - 31 August 2025

Abstract The ill-posed character of haze or fog makes it difficult to remove from a single image. While most existing methods rely on a transmission map refined through depth estimation and assume a constant scattering coefficient, this assumption limits their effectiveness. In this paper, we propose an enhanced transmission map that incorporates spatially varying scattering information inherent in hazy images. To improve linearity, the model utilizes the ratio of the difference between intensity and saturation to their sum. Our approach also addresses critical issues such as edge preservation and color fidelity. In terms of qualitative as More >

Ragini Thatikonda¹, Prakash Kodali^1,*, Ramalingaswamy Cheruku², Eswaramoorthy K.V³

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4683-4702, 2024, DOI:10.32604/cmc.2024.051844 - 20 June 2024

Abstract In the realm of low-level vision tasks, such as image deraining and dehazing, restoring images distorted by adverse weather conditions remains a significant challenge. The emergence of abundant computational resources has driven the dominance of deep Convolutional Neural Networks (CNNs), supplanting traditional methods reliant on prior knowledge. However, the evolution of CNN architectures has tended towards increasing complexity, utilizing intricate structures to enhance performance, often at the expense of computational efficiency. In response, we propose the Selective Kernel Dense Residual M-shaped Network (SKDRMNet), a flexible solution adept at balancing computational efficiency with network accuracy. A… More >

Muhammad Attique Khan^1,2, Reham R. Mostafa³, Yu-Dong Zhang², Jamel Baili⁴, Majed Alhaisoni⁵, Usman Tariq⁶, Junaid Ali Khan¹, Ye Jin Kim⁷, Jaehyuk Cha^7,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3029-3047, 2023, DOI:10.32604/cmc.2023.038838 - 08 October 2023

Abstract Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the… More >

Chunhua Liu¹, Dongfang Zou¹, Qinqin Huang¹, Shang Li², Xia Zheng¹, Xingong Li^1,*

Journal of Renewable Materials, Vol.11, No.10, pp. 3613-3624, 2023, DOI:10.32604/jrm.2023.028111 - 10 August 2023

Abstract The residual resources of ramie fiber-based textile products were used as raw materials. Ramie fiber felt (RF) was modified by NaClO₂ aqueous solution and then impregnated with water-based epoxy resin (WER). RF/WER transparent composite materials were prepared by lamination hot pressing process. The composite materials’color difference, transmittance, haze, density, water absorption, and mechanical properties were determined to assess the effects of NaClO₂ treatment and the number of ramie fiber layers on the properties of the prepared composites. The results showed significantly improved optical and mechanical properties of the RF/WER transparent composites after NaClO₂ treatment. With the increase More > Graphic Abstract

JUN SUN^1,*, MING XIE¹, JIACHEN HAO¹, NAN MAO¹, LIJING CHEN^2,*, YUANYUAN QIN³

BIOCELL, Vol.46, No.9, pp. 2145-2149, 2022, DOI:10.32604/biocell.2022.020500 - 18 May 2022

Abstract Hazelnut husk brown rot has been identified as a new disease in Liaoning Province in recent years. The objective of this study as to identify the pathogen. [Method] In this study, a standard sample of hazelnut husk brown rot was collected from Songmudao Base in Dalian City, Liaoning Province. The pathogen was identified by the studies of the morphology, pathogenicity, and analyses of ITS and LSU sequences. The pathogen was isolated and purified, which was confirmed by Koch’s postulates. The symptoms after inoculation were the same as those collected directly from a diseased tree, which More >

U. Hari, A. Ruhan Bevi^*

Intelligent Automation & Soft Computing, Vol.34, No.2, pp. 957-967, 2022, DOI:10.32604/iasc.2022.023605 - 03 May 2022

Abstract Removal of noise from images is very important as a clear, denoised image is essential for any application. In this article, a modified haze removal algorithm is developed by applying combined dark channel prior and multi-scale retinex theory. The combined dark channel prior (DCP) and bright channel prior (BCP) together with the multi-scale retinex (MSR) algorithm is used to dynamically optimize the transmission map and thereby improve visibility. The proposed algorithm performs effective denoising of images considering the properties of retinex theory. The proposed method removes haze on an image scene through estimation of the More >

Shengchun Wang¹, Peiqi Chen¹, Jingui Huang^1,*, Tsz Ho Wong²

Computer Systems Science and Engineering, Vol.40, No.3, pp. 1167-1181, 2022, DOI:10.32604/csse.2022.018911 - 24 September 2021

Abstract We propose an end-to-end dehazing model based on deep learning (CNN network) and uses the dehazing model re-proposed by AOD-Net based on the atmospheric scattering model for dehazing. Compare to the previously proposed dehazing network, the dehazing model proposed in this paper make use of the FPN network structure in the field of target detection, and uses five feature maps of different sizes to better obtain features of different proportions and different sub-regions. A large amount of experimental data proves that the dehazing model proposed in this paper is superior to previous dehazing technologies in… More >

Samia Riaz¹, Muhammad Waqas Anwar², Irfan Riaz³, Hyun-Woo Kim⁴, Yunyoung Nam^4,*, Muhammad Attique Khan⁵

CMC-Computers, Materials & Continua, Vol.70, No.1, pp. 1-17, 2022, DOI:10.32604/cmc.2022.018268 - 07 September 2021

Abstract The captured outdoor images and videos may appear blurred due to haze, fog, and bad weather conditions. Water droplets or dust particles in the atmosphere cause the light to scatter, resulting in very limited scene discernibility and deterioration in the quality of the image captured. Currently, image dehazing has gained much popularity because of its usability in a wide variety of applications. Various algorithms have been proposed to solve this ill-posed problem. These algorithms provide quite promising results in some cases, but they include undesirable artifacts and noise in haze patches in adverse cases. Some… More >

Jing Zhang^1,2, Xin Qi^3,*, San Hlaing Myint³, Zheng Wen⁴

CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 2809-2809, 2021, DOI:10.32604/cmc.2021.17410 - 26 July 2021

Abstract This article has no abstract. More >