Low-Resolution Image Enhancement Assessment
Keywords:Image Processing, Super Resolution, Image Correction, Filtering, Image Similarity,
AbstractThis study aims to address the problem with unrecognisable subject of low-quality images taken from standard resolution web cameras. These images may contain pixelated details, too much noise, and imbalance brightness and contrast. The authors used three algorithms such as Fuzzy Filter Based on Fuzzy Logic for noise reduction, Image Illumination based on Tone Mapping for uneven illumination and Super Resolution Algorithm to reconstruct the facial features of the low-resolution images. After undergoing experiment, results showed that the most acceptable filtering technique among three algorithms is Filtering Fuzzy Filter Based on Fuzzy Logic, Image Illumination Correction based on Tone Mapping for image illumination and with .60-.15-.15 Face Hallucination Super Resolution Parameter significantly improved the quality of face images taken from a low-resolution web camera. Also, results showed that high-resolution versions of low-resolution inputs significantly helped the reconstruction of facial features of low-resolution inputs. 86.67% improvement was recorded from the test images after the processing of images. Thus, the authors concluded that using the combination significantly improved the unprocessed images.
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