Efficient single image-based dehazing technique using convolutional neural networks

This research proposes a learning-based efficient single-image dehazing method. Dehazing, discriminator, and fine-tuning networks build the end-to-end network model. These three techniques are independently trained on suitable datasets. An end-to-end network architecture improves dehazing. The dehazing network model estimates transmission map, atmospheric light, and parallel convolution layers to analyze the input hazy image. The discrimination network extracted a discriminated dehazing image. Finally, discriminator network model findings are used for fine-tuning. The suggested model is tested using foggy images from various datasets and performance measures including PSNR, SSIM, MSE, and Entropy. The suggested learning-based image dehazing is compared to existing approaches qualitatively and quantitatively. The suggested approach improves PSNR by 34.3% to 3.65% over previous works. The proposed work has a 24.9% higher average SSIM and a 76% lower MSE than current efforts. The entropy of the proposed work is improved by a maximum of 9.38%.