Search results
21 sty 2021 · We select eleven optimization-suitable IQA models as perceptual objectives, and use them to optimize DNNs for four low-level vision tasks-image denoising, blind image deblurring, single image super-resolution, and lossy image compression.
15 mar 2021 · In rating participants are asked to assign a single score to the image from a pre-defined scale. Rating results depend on the selection of images for the experiment and training procedure. At the same time, ranking is much simpler as it asks a simple question — which image is better.
Through a process referred to as "rating" an image, the NIIRS is used by imagery analysts to assign a number which indicates the interpretability of a given image. The NIIRS concept provides a means to directly relate the quality of an image to the interpretation tasks for which it may be used.
12 lut 2024 · To validate the Metrics Reloaded framework, we used it to generate recommendations for common use cases in biomedical image processing (Supplementary Note 4).
This paper explores methods for quantifying image quality for automated image processing. In particular, we consider automated target detection (ATD) in gray-scale, electro-optical (EO)...
15 mar 2018 · In recent years, numbers of image quality databases annotated with subjective quality ratings have been published for evaluating and refining objective image quality assessment algorithms (Winkler 2012). More than twenty databases for image quality assessment are publicly available in the public domain at present.
21 lis 1997 · The NIIRS defines the levels of image interpretability by the types of tasks an analyst can perform with imagery of a given rating level. The NIIRS provides a simple, yet powerful, tool for...
