Image Super Resolution using Generative Adversarial Deep Learning Network

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ABSTRACT

  • The face Recognition (FR) problem is one of the significant fields in computer vision. FR is used to identify the faces that appear over distributed cameras over the network. 
  • The efficiency of face recognition systems decreases because of limited references especially (SSPP) and faces taken in the Operational Domain (OD) different from faces in the Enrollment Domain (ED) in illumination, pose, low-resolution, and blurriness. 
  • This paper proposed a method that deals with all problems related to face recognition. Besides, the design domain dictionary is used to feed different deep learning models. Face illumination transfer techniques are utilized to overcome the illumination problem. 
  • Dataset is used to train Super-Resolution Generative Adversarial Network (SRGAN) to overcome the low-resolution problem. Deblur Generative Adversarial Network (DeblurGAN) is trained the dataset to overcome the problem of blurriness.

OBJECTIVE

  • By using Generative Adversarial Network Deep Learning for Image Super-Resolution.

EXISTING SYSTEM

  • The Existing method of face recognition is processed using many types of pre-trained Convolution Neural network. 
  • The CNN method recognizes faces and classifies the particular face. This is also one of the best methods for such applications. 
  • But here the Super Resolution GAN is the advanced technique of the deep learning method. This is used to deblur the image to overcome the blurriness images.

DISADVANTAGE

  • Resize of an image or not enough to apply. 
  • Less loss of GAN function

PROPOSED SYSTEM

  • We train different deep learning approaches to identify the person's face using a design dictionary produced in the design phase. SRGAN is trained on the dataset to overcome the problem of low-resolution faces. DeblurGAN is trained on the dataset to overcome the problem of blurred faces. SRGAN and DeblurGAN work as a pre-processing step on the image that comes from the input images or camera. 
  • Check if the face photo less than size 96 x 96 (low- resolution image). Then, the SRGAN model takes the face photo and generates a high-Resolution face photo.
  •  Check if the face photo is a blurred image then the DeblurGAN model takes the face photo and generates a sharp face photo. 
  • Deep learning takes the final face photo to identify the person's face.

ADVANTAGES

  • Accurate. 
  • High Performance

ALGORITHM USED

  • SRGAN
  • DeblurGAN

SYSTEM ARCHITECTURE



SYSTEM REQUIREMENTS

  • Operating System              : Windows 7 Software 
  • Programming Package         : MATLAB R2020a

REFERENCE

  • A Novel Neural Network Method For Face Recognition Mohamed Abdelmaksoud, Emad Nabil, Ibrahim Farag, And Hala Abdel Hameed  Received May 16, 2020, accepted May 28, 2020, date of publication June 1, 2020, date of current version June 11, 2020.

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