Machine vision significantly improves the quality, efficiency ,and reliability of low quality detection of image. In the field of visual inspection, vision-based defect recognition deep learning plays important role. Basically a low quality image may lose some useful information. To eradicate this problem we use a GAN’s network. Generative adversarial networks are thriving unsupervised machine learning techniques. And GAN’s are significant in various fields such as natural language processing, computer vision. GAN’s used to reconstruct the low quality image to high quality resolution image. Basically GAN’s consists of two networks generator network, discriminator network. The work of generator network is to take input data and the input image of low quality defect image and generate high resolution image. The work of discriminator is to recognize whether the reconstructed image having high quality compared to the defect image. Working of both networks gives a final image output. Methods like PSNR, SSIM ,MSE used to calculate the quality of both degraded and high resolution image and by comparing both values we can recognize whether it is constructed properly or not.
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