A New Image Sharpening Filter Based on Gradient and Retinex-Inspired Contrast

Image edges are essential for understanding and processing the content of the acquired scene, but a clear edge detection is not always possible. Unfavorable environmental conditions, poor lighting, incorrect camera settings and/or vibrations may produce blurry or dark images with unclear content and details. Local spatial filters are integrated into many hardware and software as sharpening tools, but choosing the most adequate filter and its parameters is usually non trivial. This work proposes a new filter, whose kernel is computed by comparing over a pre-defined window the image Prewitt gradient with an image contrast measure inspired by Retinex theory. The experiments, carried out on public real-world images with different edge visibility, show that the proposed image-aware filter efficaciously increases the edge visibility with low computational cost and performs better than a standard Laplacian filter. As an usage example, the sharpening filter is here applied to medical images of retinal fundus: improving the clarity and detail of these images is essential for visualizing and analysing anatomical structures, identifying abnormalities, and assisting in diagnosis and treatments. Finally, a hardware architecture of the sharpening filter, partially integrating on-chip the gradient and contrast computation, is outlined. Such an integration could be beneficial for enhancing in real-time the quality of pictures captured by devices with limited power resources, with an average consumption of ∼ 28 μ W and ~30 kb of embedded memory in case of a color VGA image. The data obtained in filter validation experiments show a marked increase in the Prewitt’s gradient magnitudes in 100% of cases and in the edge thickness in a percentage ranging from 73.3% to 100% of cases, without visibly affecting the image naturalness.