Continuous Transformation Superposition for Visual Comfort Enhancement of Casual Stereoscopic Photography

Casual stereoscopic photography allows ordinary users to create a stereoscopic photo using two photos taken casually by a monocular camera. The visual comfort of a casual stereoscopic photo can greatly affect its visual experience. In this paper, we present a novel visual comfort enhancement method for casual stereoscopic photography via reinforcement learning based on continuous transformation superposition. We consider the transformation, in a continuous transformation space, to transform each view as superpositions of several basic continuous transformations, enabling more subtle and flexible image transformation operations to approach better solutions. To achieve the continuous transformation superposition, we prepare a collection of continuous transformation models for translation, rotation, and perspective transformations. Then we train a policy model to determine an optimal transformation chain to recurrently handle both the geometric constraints and disparity adjustment, and thereby enhance the visual comfort of casual stereoscopic images. We further propose an attention-based stereo feature fusion module that enhances and integrates the binocular information between the left and right views. Experimental results on three datasets demonstrate that our proposed method achieves superior performance to state-of-the-art methods.