Advanced discriminative transfer learning for general image restoration

Abstract

Low light images involve various techniques to procure a clear ground image. These images have various other disruptions such as mosaicking, blurring, etc. yet prevail in the image. To overcome this, the Advanced Discriminative Transfer Learning (ADTL) is proposed. This method uses novel approaches such as Discriminative transfer learning and by taking Synthetic Aperture Radar (SAR) images. Initially, the pre-processing of the image is done by increasing the intensity of the image. To the SAR images, the speckle reduction algorithm is applied, wavelet noise threshold is added, image de-noising and image reconstruction is done. Data proximal operator is used that helps a wide range of images fit into one common algorithm in DTL. Under DTL, various functionalities such as de-mosaicing, de-blurring, in-paint, de-noising, etc. is used to recover the disrupted image. These techniques help improve the quality of the image and produces a resultant image which is close to the ground image in a short span of time.