%0 Journal Article %1 gauge2011automated %A Gauge, Christophe %A Sasi, Sreela %D 2011 %E Das, Dr.Vinu V %J International Journal on Information Technology IJIT %K CBIR Gabor_transform Image_colorization %N 1 %P 3 %T Automated Colorization of Grayscale Images Using Texture Descriptors %U http://doi.searchdl.org/01.IJIT.1.1.128 %V 1 %X Image colorization has been performed through various means since the early 20th century, as a very laborious, time- consuming, subjective and painstaking manual. Its main purpose is to increase the visual appeal of old black and white photographs, motions pictures and illustrations. Current methods of image colorization can be classified into two different groups, Scribble-based and Example-based. Scribble-based colorization techniques require a user to scribble color information onto appropriate regions of the grayscale image, which is a time- consuming task 1. The color information is then spread through the image via various algorithms. Example-based colorization techniques automate this process by providing an example image from which to extract the color information from 234. This process can save a lot of time and requires little or no user interaction. However, the results can vary considerably depending on the example image chosen. Most techniques still require user input in the form of swatches, and use simple texture matching methods 3. While the method suggested by Irony et al. 2 used a very robust monochrome texture matching method with spatial filtering, they suggested that better results could be obtained by using improved spatial coherence descriptors, such as the Gabor transform. Several other research papers also suggested that better segmentation could be achieved by using Gabor filters.

@article{gauge2011automated, abstract = {Image colorization has been performed through various means since the early 20th century, as a very laborious, time- consuming, subjective and painstaking manual. Its main purpose is to increase the visual appeal of old black and white photographs, motions pictures and illustrations. Current methods of image colorization can be classified into two different groups, Scribble-based and Example-based. Scribble-based colorization techniques require a user to scribble color information onto appropriate regions of the grayscale image, which is a time- consuming task [1]. The color information is then spread through the image via various algorithms. Example-based colorization techniques automate this process by providing an example image from which to extract the color information from [2][3][4]. This process can save a lot of time and requires little or no user interaction. However, the results can vary considerably depending on the example image chosen. Most techniques still require user input in the form of swatches, and use simple texture matching methods [3]. While the method suggested by Irony et al. [2] used a very robust monochrome texture matching method with spatial filtering, they suggested that better results could be obtained by using improved spatial coherence descriptors, such as the Gabor transform. Several other research papers also suggested that better segmentation could be achieved by using Gabor filters. }, added-at = {2012-09-24T06:05:40.000+0200}, author = {Gauge, Christophe and Sasi, Sreela}, biburl = {https://www.bibsonomy.org/bibtex/256b898ac750f8c905c9d70c543fb7fd3/ideseditor}, editor = {Das, Dr.Vinu V}, interhash = {ff46e8c212731fe6b66eff2fc50b4592}, intrahash = {56b898ac750f8c905c9d70c543fb7fd3}, journal = {International Journal on Information Technology [IJIT]}, keywords = {CBIR Gabor_transform Image_colorization}, month = {march}, number = 1, pages = 3, timestamp = {2012-09-24T06:05:40.000+0200}, title = {Automated Colorization of Grayscale Images Using Texture Descriptors }, url = {http://doi.searchdl.org/01.IJIT.1.1.128}, volume = 1, year = 2011 }