Advancing Image Illumination Through Intrinsic Transfer

Intrinsic picture adjustment has also emerged as an important technique in the fast-emerging areas of computer vision and image processing to enhance the usability and interpretability of the digital images. For correcting the photo realistic-losses present in the image there are many algorithms such as NLM(Non-Local Means), Histogram, Dark channel prior, Retinex Algorithm. These algorithms couldn’t identify the boundary of the images. Comparing with the algorithms present in the literature, Gamma Algorithm shows better results in terms of PSNR and SSMI. To improve the PSNR and SSMI, a combination of Gamma and IIT(Intrinsic Image Transfer) has been proposed. Initially this algorithm identifies the hazy effects present in the image using K-Means Clustering, Guided Filter and Gamma Correction. The illumination manipulation model is employed to find the other corresponding factors, like blurred appearance and darkness of the image, by using a locally linear embedding (LLE) algorithm that is tailored to find illumination-invariant reflectance, once the haze in the image has been located by separating the sky region. LLE offers an alternative optimization technique. There is a closed-form solution for regulating image brightness being implemented using an exemplar image. Apart from computing Qualitative measures for this model PSNR and SSMI are utilized.