1,840 research outputs found
Jointly Optimizing Image Compression with Low-light Image Enhancement
Learning-based image compression methods have made great progress. Most of
them are designed for generic natural images. In fact, low-light images
frequently occur due to unavoidable environmental influences or technical
limitations, such as insufficient lighting or limited exposure time. %When
general-purpose image compression algorithms compress low-light images, useful
detail information is lost, resulting in a dramatic decrease in image
enhancement. Once low-light images are compressed by existing general image
compression approaches, useful information(e.g., texture details) would be lost
resulting in a dramatic performance decrease in low-light image enhancement. To
simultaneously achieve a higher compression rate and better enhancement
performance for low-light images, we propose a novel image compression
framework with joint optimization of low-light image enhancement. We design an
end-to-end trainable two-branch architecture with lower computational cost,
which includes the main enhancement branch and the signal-to-noise ratio~(SNR)
aware branch. Experimental results show that our proposed joint optimization
framework achieves a significant improvement over existing ``Compress before
Enhance" or ``Enhance before Compress" sequential solutions for low-light
images. Source codes are included in the supplementary material.Comment: arXiv admin note: text overlap with arXiv:2303.06705 by other author
Progressive Joint Low-light Enhancement and Noise Removal for Raw Images
Low-light imaging on mobile devices is typically challenging due to
insufficient incident light coming through the relatively small aperture,
resulting in a low signal-to-noise ratio. Most of the previous works on
low-light image processing focus either only on a single task such as
illumination adjustment, color enhancement, or noise removal; or on a joint
illumination adjustment and denoising task that heavily relies on short-long
exposure image pairs collected from specific camera models, and thus these
approaches are less practical and generalizable in real-world settings where
camera-specific joint enhancement and restoration is required. To tackle this
problem, in this paper, we propose a low-light image processing framework that
performs joint illumination adjustment, color enhancement, and denoising.
Considering the difficulty in model-specific data collection and the ultra-high
definition of the captured images, we design two branches: a coefficient
estimation branch as well as a joint enhancement and denoising branch. The
coefficient estimation branch works in a low-resolution space and predicts the
coefficients for enhancement via bilateral learning, whereas the joint
enhancement and denoising branch works in a full-resolution space and
progressively performs joint enhancement and denoising. In contrast to existing
methods, our framework does not need to recollect massive data when being
adapted to another camera model, which significantly reduces the efforts
required to fine-tune our approach for practical usage. Through extensive
experiments, we demonstrate its great potential in real-world low-light imaging
applications when compared with current state-of-the-art methods
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