An efficient image dahazing using Googlenet based convolution neural networks

The dehazing is a significant colour image-processing technique for attaining a high quality of images from haze images. Now a day’s digital cameras are playing an important key role in many applications, such as scanning, HD image generation, traffic user, tourists, especially in hilly areas satellite and radar applications. The dehazing is a complex function for digital cameras since it converts a bayers mosaic image into a final color image and then estimates the output image. The full colour image cannot be reconstructed from incomplete samples due to haze problem, and hence appropriate dehazing models are implemented to overcome this problem. In this work, a dehazing algorithm is proposed with GoogleNet deep learning mechanism for getting the improved quality of the image. In this investigation, GoogleNet deep learning model is used to reconstruct the full color image without degrading the sensitivity and resolution. In this proposed work, the deep learning based convolutional networks are realized using demosaicking for pre-processing to reproduce the dehaged full color images from the incomplete samples. In this demosaicking task, the first step is demosaicking to produce a rough image consists of unwanted color artifacts. Second step is the refining step, in which, the deep residual estimation is used to decrese the color artifacts along with the multi model fusion concept to produce good quality output images. The performance measures, viz., Peak-Signal to Noise Ratio (PSNR), Structural similarity Index (SSIM), and Mean Square Error (MSE) are evaluated and compared with existing models. The PSNR is 32.78, SSIM is 0.9412, MSE is 0.098, F1-score is 0.989, sensitivity is 0.972, and CC is 0.978 have been attained by this optimized algorithm. The GoogleNet technology outperforms the existing methods. This deep learning mechanism does process the input hazy images and decomposes the smoothness hazy free elements from texture elements.