MS-UNet: A Deep Learning Framework for High-Fidelity Image Dehazing

The visibility of many technological applications, such as autonomous driving, remote sensing, and video monitoring, is severely hampered by hazy weather conditions, which are made worse by rising global pollution. MS-UNet (Multi-Scale U-Net), a revolutionary deep learning framework for high-fidelity image dehazing, is presented in this research. Through the utilization of a multi-scale U-Net architecture, the suggested technique successfully tackles atmospheric distortions and clarifies blurry images. To improve the model’s capacity to recover haze-free photos, the framework uses a multi-scale feature extraction approach that records contextual information as well as fine-grained features. By obtaining better Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) values on the SOTS dataset, quantitative assessments show that MS-UNet performs better than a number of cutting-edge dehazing methods. The framework makes a substantial addition to the fields of computer vision and image processing, as evidenced by qualitative results that further demonstrate its potential to generate aesthetically pleasing dehazed images.