Paper Title : Deeping Learning the Transformation Process for Fast Image Enhancement

ISSN : 2394-2231
Year of Publication : 2020

10.29126/23942231/IJCT-v7i5p4

Authors: Mengcheng Xiang,Jinguang Chen

         



MLA Style: Mengcheng Xiang,Jinguang Chen " Deeping Learning the Transformation Process for Fast Image Enhancement " Volume 7 - Issue 5 September - October,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

APA Style: Mengcheng Xiang,Jinguang Chen " Deeping Learning the Transformation Process for Fast Image Enhancement " Volume 7 - Issue 5 September - October,2020 International Journal of Computer Techniques (IJCT) ,ISSN:2394-2231 , www.ijctjournal.org

Abstract
-We propose a novel deep learning-based image enhancement method which learn the transformation function or matrix instead the image content to achieve fast enhancement of the image. The structure of the Network used in this work utilizes four residue blocks of the ResNet-18 as the basenet to train the transformation function or matrix of the input image F, and outputs the transformation matrix H, thus this net is also named FHNet. With the learned information contained in the transformation matrix H, the enhancement of the image feature is easily implemented with multiplying the transformation matrix with the input images, that is G=HF. This method is proved to be effective for the feature enhancement of the image, and which can process the image enhancement with fast speed and high quality. Compared with conventional method, our proposed method can effectively solve the problem of color distortion and characters with small memory consumption, low computation cost and high speed.

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Keywords
Image Enhancement, Deep learning, FHNet.