Learning-Based Restoration of Backlit Images
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Backlighting is a commonly encountered ill illumination condition that can cause serious degradation of image quality. In this paper, we propose a learning-based spatially adaptive technique of optimal tone mapping to restore backlit images. Object surfaces illuminated from behind in a scene are detected by a soft binary classifier that is constructed via supervised learning. Two optimal tone mapping functions, one for backlit regions and the other for the remainder of the image, are used and their outputs are fused to restore illegible surface details in backlit regions and at the same time improve contrast in overexposed regions, if any. Experimental results demonstrate the superior performance of the proposed new technique over existing image enhancement techniques on backlit photographs.
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