SMGAN: A self-modulated generative adversarial network for single image dehazing

Abstract

Single image dehazing has become a key prerequisite for most high-level computer vision tasks since haze severely degrades the input images. Traditional prior-based methods dehaze images by some assumptions concluded from haze-free images, which recover high-quality results but always cause some halos or color distortion. Recently, many methods have been using convolutional neural networks to learn the haze-relevant features and then retrieve the original images. These learning-based methods achieve better performance in synthetic scenes but can hardly restore a clear image with discriminative texture when applied to real-world images, mainly because these networks are trained on synthetic datasets. To solve these problems, a self-modulated generative adversarial network for single image dehazing named SMGAN is proposed. The SMGAN inputs prior-dehazed images into a parameter-shared encoder to produce some latent information of these dehazed images. During the hazy image decoding process, the latent information is sent to self-modulated batch normalization layers, which makes the network fit in real haze removal. Moreover, consider that there are some over-enhanced regions in the guidance images, and a refine module is proposed to alleviate the negative information. The proposed SMGAN combines the advantages of prior-based methods and learning-based methods, which provides superior performance compared with the state-of-the-art methods on both synthetic and real-word datasets.

Systematic Review of Advanced AI Methods for Improving Healthcare Data Quality in Post COVID-19 Era

“…Based on qualitative analysis of image quality by experienced radiologists, images de-noised using RED-CNN had improved artifact reduction, noise suppression, contrast retention, lesion discrimination, and overall quality compared to baseline methods such as dictionary learning, non-local means, and the previously published algorithm by Chen et al Unlike previous deep learning image de-noising models, RED-CNN also had improved PNSR compared to all baselines. In 2018, Structurally-Similar Multi-Scale Generative Adversarial Network (SMGAN-3D) [157] was also introduced as novel GAN-based model for LDCT image de-noising, but it did not achieve significant improvements over RED-CNN. Dilated Residual Learning (DRL) [158] is a CNNbased model introduced in 2019 which uses an edge detection convolution layer to identify object boundaries and uses dilated convolution layers to capture more contextual information from the input image in fewer layers to make computation less expensive.…”