@InProceedings{6952028,
  author = {A. Stojkovic and Z. Ivanovski},
  booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},
  title = {Motion estimation for Super-resolution based on recognition of error artifacts},
  year = {2014},
  pages = {246-250},
  abstract = {The work presents an effective approach for subpixel motion estimation for Super-resolution (SR). The objective is to improve the quality of the estimated SR image by increasing the accuracy of the motion vectors used in the SR procedure. The correction of the motion vectors is based on appearance of error artifacts in the SR image, introduced due to registration errors. First, SR is performed using full pixel accuracy motion vectors obtained using full search block matching algorithm (FS-BMA). Then, machine learning based method is applied on the resulting images in order to detect and classify artifacts introduced due to missing subpixel components of the motion vectors. The outcome of the classification is a subpixel component of the motion vector. In the final step, SR process is repeated using the corrected (subpixel accuracy) motion vectors.},
  keywords = {image classification;image matching;image registration;image resolution;learning (artificial intelligence);motion estimation;motion estimation;SR image quality improvement;super resolution;error artifact recognition;motion vector correction;full search block matching algorithm;machine learning based method;registration error;artifact classification;subpixel component classification;Vectors;Image resolution;Accuracy;Support vector machine classification;Motion estimation;Image edge detection;Feature extraction;super-resolution;image registration;machine learning;artifacts detection},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926251.pdf},
}

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The correction of the motion vectors is based on appearance of error artifacts in the SR image, introduced due to registration errors. First, SR is performed using full pixel accuracy motion vectors obtained using full search block matching algorithm (FS-BMA). Then, machine learning based method is applied on the resulting images in order to detect and classify artifacts introduced due to missing subpixel components of the motion vectors. The outcome of the classification is a subpixel component of the motion vector. In the final step, SR process is repeated using the corrected (subpixel accuracy) motion vectors.","keywords":"image classification;image matching;image registration;image resolution;learning (artificial intelligence);motion estimation;motion estimation;SR image quality improvement;super resolution;error artifact recognition;motion vector correction;full search block matching algorithm;machine learning based method;registration error;artifact classification;subpixel component classification;Vectors;Image resolution;Accuracy;Support vector machine classification;Motion estimation;Image edge detection;Feature extraction;super-resolution;image registration;machine learning;artifacts detection","issn":"2076-1465","month":"Sep.","url":"https://www.eurasip.org/proceedings/eusipco/eusipco2014/html/papers/1569926251.pdf","bibtex":"@InProceedings{6952028,\n author = {A. Stojkovic and Z. Ivanovski},\n booktitle = {2014 22nd European Signal Processing Conference (EUSIPCO)},\n title = {Motion estimation for Super-resolution based on recognition of error artifacts},\n year = {2014},\n pages = {246-250},\n abstract = {The work presents an effective approach for subpixel motion estimation for Super-resolution (SR). The objective is to improve the quality of the estimated SR image by increasing the accuracy of the motion vectors used in the SR procedure. The correction of the motion vectors is based on appearance of error artifacts in the SR image, introduced due to registration errors. First, SR is performed using full pixel accuracy motion vectors obtained using full search block matching algorithm (FS-BMA). Then, machine learning based method is applied on the resulting images in order to detect and classify artifacts introduced due to missing subpixel components of the motion vectors. The outcome of the classification is a subpixel component of the motion vector. 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