Data-driven Automatic Edge Sharpening

edge-sharpening;cumulative logistic regression;Image Enhancement;Perception

Nam, Myra ; Ahuja, Narendra ; Ahuja ; Narendra

关键词: edge-sharpening;    cumulative logistic regression;    Image Enhancement;    Perception;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/14685/nam_myra.pdf?sequence=2&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Sharpening is a powerful image transformation because sharp edges can bring out image details. Sharpness is achieved by increasing local contrast and reducing edge widths.We present a method that enhances sharpness of images and thereby their perceptual quality.Most existing enhancement techniques require user input to improve the perception of the scene in a manner most pleasing to the particular user. Our goal of image enhancement is to improve the perception of sharpness in digital images for human viewers. We consider two parameters in order to exaggerate the differences between local intensities. The two parameters exploit local contrast and widths of edges. We start from the assumption that color, texture, or objects of focus such as faces affect the human perception of photographs.When human raters are presented with a collection of images with different sharpness and asked to rank them according to perceived sharpness, the results have shown that there is a statistical consensus among the raters.We introduce a ramp enhancement technique by modifying the optimal overshoot in the ramp for different region contrasts as well as the new ramp width. Optimal parameter values are searched to be applied to regions under the criteria mentioned above. In this way, we aim to enhance digital images automatically to create pleasing image output for common users.