The High Dynamic Range Imaging (HDRI) pipeline provides us with greater level of detail in color and brightness pixel values by introducing methods from image/video acquisition, storage and compression, to display and quality evaluation. HDRI techniques are considered to offer massive potential for other computer vision and graphics applications to leverage the higher range of information which have not been the main purpose of the HDRI pipeline so far. In this work, we focus on two important computer vision problems and introduce new techniques and algorithms to take HDRI into account to solve them. Stereo Matching: State of the art stereo matching and 3D reconstruction approaches do not work as well in HDR scenes and low textured regions. The greater luminance information available in HDR content is taken into account to provide more informative disparity maps in tricky matching regions of stereo images. ^Our combinational approaches close the gap between available HDR methods and stereo matching. We introduce an HDR stereo data set as well as a ground truth HDR stereo matching algorithm as a reference for our tone mapped stereo matching benchmarking. HDR stereo images were captured, calibrated and rectified. A combinational tone mapping approach is proposed for the most effective disparity map computation in HDR scenes. Our combinational tone mapped stereo matching method reduces the stereo matching errors by a factor of three. Mesopic Rendering: Human visual perception varies in different ambient conditions. Our color and contrast perception especially in darker environments is influenced by the combination of rods and cones photoreceptors. Available standard rendering methods do not take this important perceptual effect into account. HDR images cover the full luminance range of scenes from photopic and mesopic vision to schotopic vision. ^In the second part of our research we focus more on color and contrast perception in mesopic vision based on HDRI concepts. As another usage of tone mapped images, effective content rendering in dark environments such as Virtual Reality (VR) head mounted displays, night time driving and watching TV in the dark is proposed. Furthermore, subjective evaluations were performed to suggest an effective tone reproduction along with a color retargeting method to compensate for visual detail loss in dark environments on dimmed displays. In the subjective studies our perceptual results were ranked as the most preferred solution in more than 95% of the cases.