Papers for

camera firmware engineers

Papers whose findings have a practical use for this group, as judged from the abstract. Open a paper to read what it means in practice.

Illumination conditioning improves low-light image enhancement in ssm models

Stabilizing Illumination Conditioning in SSM-Based Low-Light Image Enhancement with Bottleneck Prompt Sidecars

Abstract: Low-light image enhancement (LLIE) requires more than brightness amplification because real low-light images often exhibit spatially varying illumination, amplified noise, color distortion, and local detail loss. Recent SSM- and Mamba-based restorers provide efficient long-range modeling, yet the route for introducing illumination priors into such backbones remains insufficiently studied. This study addresses safe illumination conditioning for SSM-based LLIE through a bottleneck-side PromptAdapter. A lightweight illumination estimator first predicts a single-channel illumination map, which is then converted into prompt tokens and injected through a residual sidecar adapter at the bottleneck, thereby avoiding direct modification of the state-space core. Under a locked three-seed evaluation protocol on LOL-v1, LOLv2-Real, and LOLv2-Synthetic, the PromptAdapter improves over the Base model by 0.94 dB, 1.24 dB, and 0.93 dB in PSNR, respectively, while also improving SSIM, LPIPS, and masked dark-region metrics. The clearest real-data evidence appears on LOLv2-Real. A Learned-Fixed control on LOL-v1 indicates that the present results support the utility of the sidecar conditioning pathway more strongly than a strict claim of illumination-conditioned superiority, and the LOLv2-Synthetic result remains stability-conditioned. These findings suggest that, in SSM-based LLIE, conditioning design can be as important as the conditioning signal itself.

Fri 16 OctComputer Vision and Pattern Recognition
The gist
Improving low-light photos is more complicated than just making them brighter because shadows, noise, and color issues vary across the image. The authors propose a new way to add illumination information safely into a special type of image model called an SSM via a small add-on called a bottleneck-side PromptAdapter. This approach predicts a map of lighting and uses it to guide the model without changing its core structure. Tests on standard low-light image datasets showed consistent improvements in image quality metrics when using this method. The authors caution that how the conditioning is designed is as important as the lighting information itself.
Open → 2610.22958v1

Visual autoregressive priors improve raw to sRGB color and detail recovery

Visual Autoregressive Priors for RAW-to-sRGB Image Signal Processing

Abstract: RAW-to-sRGB image signal processing (ISP) must recover perceptually faithful colors and fine details from sensor measurements, often under imperfect spatial alignment and missing camera metadata. This paper presents, to the best of our knowledge, the first application of visual autoregressive (VAR) next-scale prediction over a discrete image codebook to the RAW-to-sRGB ISP task. We adapt a frozen 1.10\,B-parameter VAR backbone for RAW-conditioned ISP with only 32.93\,M trainable parameters (2.99\%), and propose a frequency-decomposed color loss that separately supervises low-frequency tone via wavelet LL cosine similarity and chromatic edges via detail-band $\ell_1$. On the Zurich RAW-to-sRGB benchmark, the method improves PSNR-Y from 21.31 to 21.89\,dB and reduces LPIPS from 0.276 to 0.218 on the full 1,204-image test set. Diagnostic experiments show that the VAR prior preserves structure well, but continuous color transfer remains the dominant bottleneck: oracle affine correction recovers 3.8\,dB, while learned color heads yield marginal gains.

Wed 16 SeptComputer Vision and Pattern RecognitionMultimedia
The gist
Converting raw camera sensor data into colorful, detailed images is tricky because of missing or imperfect camera settings. The authors use a visual autoregressive model, which predicts image details in a step-by-step way based on learned patterns from many images. They also introduce a new way to measure color and detail quality in different frequency bands. This approach makes images look closer to real photos by better preserving structure, though perfect color matching is still difficult. Tests on a standard dataset show improved image quality compared to previous methods.
Open → 2609.18302v1