Under arbitrary illumination of an object, human eyes estimate the illuminant using integrated judgment; that is, the scene is corrected by the human visual system. However, artificial imaging systems are unable to recover images without access to the original illuminant, thus digital cameras include embedded color constancy algorithms. To solve this problem, we propose an illuminant compensation method using a camera noise analysis without segmentation.
Changes in the ambient conditions vary the sensitivities of the human visual system when watching a display, resulting in different perceptions under altered viewing conditions despite the same stimulus. Therefore, since a viewerĄ¯s peripheral environment can be affected by artificial illumination, daylight, or fading light, algorithms are needed that can reproduce colors on a display under various types of ambient lightings as the viewing conditions are always changing.
Image stitching has recently been attracting interest as an effective way of increasing the restricted field of view of a camera by combining a set of separate images into a single seamless image. This technique has already been widely applied to such areas as video compression, video indexing, image alignment, and panoramic videos, where panoramic technology in particular has been applied to lens falloff, exposure mismatches, and vignettes. As such, this paper focuses on how to derive clues from an image to implement panoramic technology.
Sunlight is transmitted to the observer through absorption, scattering, and re-emission in the atmosphere. Thus, in the case of inclement weather, images captured by a camera are degraded by haze that is dependent on the depth. In this study, the focus is a dehazing method for a single image.