Image dehazing increases the clarity of the visual aspect of a driver using a vehicle; when surveilling; and during consumer photographic use. Dehazing using CNN and the physics-based method has no method of understanding the entire global image scene; and both dehazing methods fail to perform in non-homogeneous haze setup, and finally transformer-based methods require either very large scale datasets or lots of processing power to accomplish their tasks. This research presents a unified framework for single-image dehazing named Physically Guided Wavelet Swin Network (PhyWave-Swin). PhyWave-Swin combines the necessary components of physical interpretability, multifrequency analysis and attention driven global modelling to advance this area of work. PhyWave-Swin was developed using the RESIDE dataset and underwent evaluation for both synthetic and real-world test data. The resulting performance metrics for PhyWave-Swin are 32.84/0.946 PSNR/SSIM respectively for SOTS-Indoor and 30.29/0.939 PSNR/SSIM respectively for SOTS-Outdoor test data; and PhyWave-Swin produces considerably better perceptual image quality versus other dehazing work that produces NIQE=3.65 and BRISQUE=33.9 in realworld scenarios. Therefore; a combination of physics, wavelet frequency representation and transformer-based global modelling provide the PhyWave-Swin with a very effective solution to single-image dehazing.