Optimal Methods Research On Grayscale Image Interpolation

Image interpolation process requires a reference or source image to construct a new image whose size is controlled by the interpolation ratio selected or set. This process has been a problem of prime importance in many fields due to its wide application in satellite imagery, biomedical imaging, and particularly in military and consumer electronics domains. Interpolating a digital image amounts to creating empty spaces in the source image and filling in with the guessed pixel values. If the guessing technique is not of high accuracy, it would result in a low resolution image with visible artefacts and missing details. With huge image databases, creating high resolution from the low resolution image is time-consuming. However, image data interpreted directly from sensors often come up with insufficient details whereas in some cases it is of high importance to visualize sufficiently those image details. In addition, with the advances on multimedia systems, users can retrieve data quickly with a relative ease for neat browsing and rapid availability of such data.In this dissertation, I present the results of a 3 years research study on image interpolation techniques and algorithms-improving the performance in speed and PSNR.Firstly, I proposed two algorithms for high resolution image interpolation based on ACA. The idea to use the ACA, to deal with image interpolation problem, originated from the work presented in [118] about analog circuits faults detection and isolation. The OBACA algorithm used ACA to determine the potent pixels that would contribute in improving the output image resolution after interpolation. Experimentally, the proposed scheme yielded better results than conventional bilinear interpolation. The second AACA algorithm introduced the weights of image pixels as function of pheromone trails dropped by ants according to exploitation and exploration mechanism. Experimentally, AACA was found to be faster than OBACA algorithm. The PSNR also remained superior to all other algorithms compared with.Secondly, I proposed three image interpolation algorithms based on the pixel value corresponding to the smallest absolute difference for high resolution image interpolation using less computational efforts. The first proposed SAD algorithm was based on reprocessing one of the four pixels surrounding the unknown-value location and calculating the mean between that pixel and the value created by the conventional bilinear algorithm and by multiplying the mean the control factor k whose value was selected according to experimental analysis. Experimentally, SAD showed the effectiveness and higher performances only in terms of the PSNR when compared to the conventional nearest, bilinear and bicubic interpolation algorithms. The SAD improvement resulted in ASAD algorithm which replicated directly the pixel value corresponding to the smallest absolute difference to the unknown-value location. Experimentally, ASAD demonstrated higher in terms of MET and relatively PSNR than SAD. Finally, I proposed the NNV algorithm which achieved higher results than both SAD and ASAD using the mode calculation scheme. Experimentally, the results obtained were superior to those provided by SAD and ASAD.Thirdly, I proposed two speedy image interpolation algorithms, one based on the pythagorean theorem and another on reducing the number of pixel groups undergoing the bilinear weighted average operations have been introduced for high quality scaling of digital images. The latter showed that a simple theoretical analysis can lead to an improvement in PSNR and MET when compared to conventional bilinear (BI) interpolation by reducing the number of the BI weighted average operations while the pythagorean based algorithms replaced the hypotenuse and catheti with image pixels absolute difference. The two proposed algorithms demonstrated higher performances in terms of the processing speed as well as PSNR in some cases, when compared to the conventional interpolation algorithms mentioned.Finally, I proposed an image edge thinning scheme based on ACO and its application to digital image interpolation. The proposed edge thinning scheme was applied, after two processes, that is, the linear interpolation and the ACO edge detection have been completed. The thinning process was modeled like the Traveling Salesman Problem (TSP) before applying the ACO to find the thinnest ridge according to some rules set and the process of modifying/removing the unwanted pixels information was conducted in parallel. Experimentally, the edge thinning based interpolation demonstrated higher performances in terms of high resolution/quality and visibly outperformed other interpolation algorithms mentioned but at increased computational efforts. The methods presented in this dissertation permit to achieve High resolution (H.R) and either speedy image interpolation. Experiments showed that the proposed methods are able to increase the interpolation PSNR and speed. Future developments of the proposed methods may be adapted to color and real-time low resolution (L.R) images interpolation.