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

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 >

Di Wang, Bingcai Wei, Liye Zhang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1989-2000, 2023, DOI:10.32604/cmes.2023.028529

Abstract Extracting useful details from images is essential for the Internet of Things project. However, in real life, various external environments,such as badweather conditions,will cause the occlusion of key target information and image distortion, resulting in difficulties and obstacles to the extraction of key information, affecting the judgment of the real situation in the process of the Internet of Things, and causing system decision-making errors and accidents. In this paper, we mainly solve the problem of rain on the image occlusion, remove the rain grain in the image, and get a clear image without rain. Therefore,… More >