This paper proposes a novel method for modeling retinal cone distribution in humans. It is based on Blue-noise sampling algorithms being strongly related with the mosaic sampling performed by cone photoreceptors in the human retina. Here we present the method together with a series of examples of various real retinal patches. The same samples have also been created with alternative algorithms and compared with plots of the center of the inner segments of cone photoreceptors from imaged retinas. Results are evaluated with different distance measure used in the field, like nearest-neighbor analysis and pair correlation function. The proposed method can effectively describe features of a human retinal cone distribution by allowing to create samples similar to the available data. For this reason, we believe that the proposed algorithm may be a promising solution when modeling local patches of retina.

Optical systems in digital cameras present a limit during the acquisition of standard and High Dynamic Range Images (HDRI) due to the presence of veiling glare, an artifact caused by an unwanted spread of the source of light. In this paper, we analyze the state-of-the-art of veiling glare removal in HDRI, giving attention to the paper presented by Talvala. Then we describe an algorithm for veiling glare removal based on the same occlusion mask, to study the benefits provided by it in HDRI acquisition process. Finally, we demonstrate the efficiency of the occlusion mask method in veiling glare removal without any post production estimation and subtraction.

It is estimated that about 5–10% of the male population has some kind of color vision deficiency (CVD). For them, it is difficult or even impossible to distinguish certain colors. Many image enhancers exist, mostly based on hue changes, since CVDs are usually modeled at spectral level. In this article, the authors consider another point of view, investigating the role of luminance contrast to treat CVD. In the following, the authors present a test, administered as a mobile application, to assess the performance of SiChaRDa, a recently proposed image enhancer, inspired by a model of the human visual system, that modifies the lightness of the image. The results indicate a role of contrast and edges in the readability of images for color vision-deficient people; however, they do not support a clear and unambiguous interpretation.

This paper proposes a novel method for modeling human retinal cone distribution. It is based on Blue-noise sampling algorithms that share interesting properties with the sampling performed by the mosaic formed by cone photoreceptors in the retina. Here we present the method together with a series of examples of various real retinal patches. The same samples have also been created with alternative algorithms and compared with plots of the center of the inner segments of cone photoreceptors from imaged retinas. Results are evaluated with different distance measure used in the field, like nearest-neighbor analysis and pair correlation function. The proposed method can describe features of a human retinal cone distribution with a certain degree of similarity to the available data and can be efficiently used for modeling local patches of retina.