Synthetically generated fiber pixilated image database

• Main Authors: Anant, Shinde, Matham, Murukeshan Vadakke
• Other Authors: Kalli, Kyriacos
• Format: Conference or Workshop Item
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Mechanical engineering
• Online Access: https://hdl.handle.net/10356/103904; http://hdl.handle.net/10220/20135
• Institution: Nanyang Technological University

Visual access to physically inaccessible parts has become the forefront of research and development in medical diagnostics tools and procedures. Flexible and thin endoscopes with fiber bundle as an image conduit serves this purpose. However, when the light passes through the core of the fiberlet, it is blocked by the inter fiberlet gap. This structural limitation creates special honeycomb like pattern overlaying the image captured with the image fiber assisted probes, known as the comb structure or fiber pixelation. It obstructs the perception of the original image sacrificing resolution and contrast and inhibits the use of object recognition and tracking algorithms. Generally, comb structure removal or depixelation methods are employed to remove honeycomb pattern from an image. In the recent past, several depixelation techniques have been proposed albeit using different set of pixilated images by different researchers. It is quite difficult to make a comparison of their performances based on such images, as they adopt different images for different particular framework of their study. In this context, a basic database of such images is the need of the hour to meet the growing diagnostic needs in the medical and industrial arena. This paper in this context proposes and details a Comb Structure Affected Image database (CSAI) to meet the objective. Images are generated considering the image fiber specifications and the characteristics at different targeted optical imaging modalities delineated by resolution scales. The proposed database is designed to have a set of synthetically generated pixelated images of test patterns of different scales, sizes and shapes.