LatentBurst: A Fast and Efficient Multi Frame Super-Resolution for Hexadeca-Bayer Pattern CIS images
Sangwook Baek, Vin Van Duong, Karam Park, and Pilkyu Park

TL;DR
This paper presents LatentBurst, a fast multi-frame super-resolution network for hexadeca-Bayer CIS images, addressing demosaicing, denoising, and fusion challenges for real-time mobile device applications.
Contribution
The paper introduces a novel, efficient network architecture with pyramid alignment and knowledge distillation tailored for hexadeca-Bayer pattern super-resolution.
Findings
Outperforms state-of-the-art methods in various scenarios
Operates in real-time on mobile devices
Effectively handles large motion and misalignment
Abstract
This paper introduces a novel multi frame super-resolution network (MFSR) for burst hexadeca Bayer pattern Contact Image Sensor (CIS) images, which includes demosaicing, denoising, multi-frame fusion, and super-resolution. Designing a high-quality reconstruction network poses several challenges as follows: 1) Unlike the Bayer color filter array (CFA) pattern, it is hard to interpolate hexadeca-Bayer pattern since the pixel distance between the same color groups increases; 2) Due to large object motion and camera movements, the final fusion result usually suffers the misalignment resulting a blurry image or ghosting artifacts; 3) The proposed network should be fast and efficient enough to operate in real-time on mobile devices. To overcome these challenges, we propose a novel network, called LatentBurst, which contains: 1) a pyramid align and fusion approach in latent feature to deal…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
