Personalized image enhancement method for color deficient observers

A personalized image enhancement method is proposed to improve color vision in hereditary color vision deficiency (CVD). It is divided into two stages: evaluation of CVD and gamut mapping for image enhancement. These two separate stages are connected via a psychophysical experiment, through which the deficiency test result expressed using the C-index can be further transformed into a physical parameter, namely the wavelength shift of the cone fundamental. Experiments conducted by the color-deficient observers (CDOs) validated this proposed method, and it is emphasized that the proposed method is just serving as a template for image enhancement. A more advanced simulation model, a more accurate assessment method, or a more sophisticated gamut mapping algorithm can yield a better result.

Gamut mapping based image enhancement algorithm for color deficiencies

This study presents an approach for enhancing color images for color vision deficiencies. The proposed approach is separated into three stages. First, the type and severity of a color deficient observer (CDO) were evaluated. Following that, the perceived color gamut was assessed using a physiologically-based color deficiency simulation model. Finally, images prepared for color normal observers (CNOs) were re-colored using a gamut mapping method to map colors from the gamut of a CNO to that of a CDO. Two psychophysical experiments were carried out to validate this method, and the results suggest that it is a promising solution for the CDOs. The unique feature of the present method is to include a gamut mapping method to enhance the color discrimination by preserving the perceived hue.

Study of a retinal layer model to generate a spike waveform for a color deficient and strabismus individual

The physiological modeling of retinal layers to provide an insight into how the incoming image is converted into its equivalent spike train that can be decoded by the human brain is a key issue. Most of the retinal layer models concentrate mainly on image compression, edge detection and image reconstruction. A retinal layer model to generate spike waveform corresponding to the visual information is not covered much in the literature. The aim of this study was to develop a mathematical model of retinal layers that has complex neural structures, that can detect the incoming signal and transform the signal into the equivalent spike train. The proposed retinal layer model includes a photoreceptor, an outer plexiform (OPL), an inner plexiform (IPL) and ganglion cell layers exhibiting the properties of compression, luminance and spatial temporal filtering in the processing of visual information. The photoreceptor layer enhances the contrast visibility in the dark region and maintains the same in the bright regions. The OPL is modeled to enhance the contour of the image. The finer detail of the image is extracted by mathematically modeling the IPL. The ganglion cell layer is modeled using the Hodgkin-Huxley model to generate the spike train for the incoming information. The spike train was generated for color deficient individuals namely protanopia, deuteranopia, tritanopia and for individuals suffering from night blindness. Simulation results showed a spike train was generated only for a certain threshold stimulus value. The differences in spike pattern for a normal and visually impaired individual were studied. This may lead to a methodology for earlier diagnosis.