Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework

• URL: http://arxiv.org/abs/2605.07429v2
• Date: Mon, 11 May 2026 08:03:05 GMT
• Title: Towards Photorealistic and Efficient Bokeh Rendering via Diffusion Framework
• Abstract summary: Existing mobile devices are constrained by compact optical designs, such as small apertures.<n>We propose MagicBokeh, a unified diffusion-based framework designed for high-quality and efficient bokeh rendering.
• Score: 29.580886518291518
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing mobile devices are constrained by compact optical designs, such as small apertures, which make it difficult to produce natural, optically realistic bokeh effects. Although recent learning-based methods have shown promising results, they still struggle with photos captured under high digital zoom levels, which often suffer from reduced resolution and loss of fine details. A naive solution is to enhance image quality before applying bokeh rendering, yet this two-stage pipeline reduces efficiency and introduces unnecessary error accumulation. To overcome these limitations, we propose MagicBokeh, a unified diffusion-based framework designed for high-quality and efficient bokeh rendering. Through an alternative training strategy and a focus-aware masked attention mechanism, our method jointly optimizes bokeh rendering and super-resolution, substantially improving both controllability and visual fidelity. Furthermore, we introduce degradation-aware depth module to enable more accurate depth estimation from low-quality inputs. Experimental results demonstrate that MagicBokeh efficiently produces photorealistic bokeh effects, particularly on real-world low-resolution images, paving the way for future advancements in bokeh rendering. Our code and models are available at https://github.com/vivoCameraResearch/MagicBokeh.

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