Depth-Guided Dense Dynamic Filtering Network for Bokeh Effect Rendering

Kuldeep Purohit, Maitreya Suin, Praveen Kandula, Rajagopalan Ambasamudram

Abstract: Bokeh effect refers to the soft defocus blur of the background, which can be achieved with different aperture and shutter settings in a camera. In this work, we present a learning-based method for rendering such synthetic depth-of-field effect on input bokeh-free images acquired using ordinary monocular cameras. The proposed network is composed of an efficient densely connected encoder-decoder backbone structure with a pyramid pooling module. Our network leverages the task-specific efficacy of joint intensity estimation and dynamic filter synthesis for the spatially-aware blurring process. Since the rendering task requires distinguishing between large foreground and background regions and their relative depth, our network is further guided by pre-trained salient-region segmentation and depth-estimation modules. Experiments on diverse scenes show that our model elegantly introduces the desired effects in the input images, enhancing their aesthetic quality while maintaining a natural appearance. Along with extensive ablation analysis and visualizations to validate its components, the effectiveness of the proposed network is also demonstrated by achieving the second-highest score in the AIM 2019 Bokeh Effect challenge: fidelity track.