Removal of shadows from a single image is a challenging problem. Producing a high-quality shadow-free image which is indistinguishable from a reproduction of a true shadow-free scene is even more difficult. Shadows in images are typically affected by several phenomena in the scene, including physical phenomena such as lighting conditions, type and behavior of shadowed surfaces, occluding objects, etc. Additionally, shadow regions may undergo post-acquisition image processing transformations, e.g., contrast enhancement, which may introduce noticeable artifacts in the shadow-free images. We argue that the assumptions introduced in most studies arise from the complexity of the problem of shadow removal from a single image and limit the class of shadow images which can be handled by these methods. The purpose of this paper is twofold: First, it provides a comprehensive survey of the problems and challenges which may occur when removing shadows from a single image. In the second part of the paper, we present our framework for shadow removal, in which we attempt to overcome some of the fundamental problems described in the first part of the paper. Experimental results demonstrating the capabilities of our algorithm are presented.

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Empathy allows us to respond to the emotional state of another person. Considering that an empathic interaction may last beyond the initial response, learning mechanisms may be involved in dynamic adaptation of the reaction to the changing emotional state of the other person. However, traditionally, empathy is assessed through sets of isolated reactions to another's distress. Here we address this gap by focusing on adaptive empathy, defined as the ability to learn and adjust one's empathic responses based on feedback. For this purpose, we designed a novel paradigm of associative learning in which participants chose one of two empathic strategies (reappraisal or distraction) to attenuate the distress of a target person, where one strategy had a higher probability of relieving distress. After each choice, participants received feedback about the success of their chosen strategy in relieving the target person's distress, which they could use to inform their future decisions. The results show that the participants made more accurate choices in the adaptive empathy condition than in a non-social control condition, pointing to an advantage for learning from social feedback. We found a correlation between adaptive empathy and a trait measure of cognitive empathy. These findings indicate that the ability to learn about the effectiveness of empathic responses may benefit from incorporating mentalizing abilities. Our findings provide a lab-based model for studying adaptive empathy and point to the potential contribution of learning theory to enhancing our understanding of the dynamic nature of empathy.