Computationally Efficient CNN Based Smart Dehaze Net for Haze Removal of Biomedical Images

Artificial Intelligence has made significant strides recently especially in the field of computer vision. It played a pivotal role in revolutionising the automation, reliability and robustness of the most modern biomedical applications like CT scan, PET scan, Fundoscopy etc. Due to the wide spread use of camera sensors for examining the internal organs like Liver, Gall bladder, Retina etc, haze free images are absolutely essential. Most of the images captured by the visual sensors have haze introduced due to various environmental factors like suspended particles in air which badly affects the fidelity of the images captured by camera sensors. Image dehazing is a process of removing the haze and thereby improving the overall image quality. Image dehazing finds extensive applications especially in the fields of surveillance systems, defence, air traffic control, traffic cameras, self-driven cars, under water imaging etc. In this paper a computationally efficient CNN architecture with eight convolution and three concatenation layers is proposed which removes the haze without much degradation in colour and contrast of the image. The Proposed model is trained on NYU2 dataset and provided a 166% hike in the PSNR when compared with the best of the available haze removal techniques. Also the BRISQUE value and entropy of the haze removed image went up by 80.95 %. and 100.78% respectively over the current state of the art haze removal techniques. Also the model shown 59% reduction in computational time.