Towards Low-Cost Learning-based Camera ISP via Unrolled Optimization
Bibliographic record
Abstract
Recently, learning-based image signal processor (ISP) pipelines modeled using convolutional neural networks (CNNs) have been able to provide higher quality images over traditional model-based ISPs at the expense of significant memory, energy, and computation overhead. We propose an unrolled optimization network that models the ISP pipeline with considerably lower number of parameters and computation overhead. The unrolled optimization solves the image reconstruction problem of the ISP by leveraging both model-based and learning-based methods. In the proposed ISP model, the image formation operators namely, blur kernels and sensor sampling functions are formulated with learnable parameters such that the physical constraints are respected during conventional training. A CNN that is shared across the iterations of the unrolled model plays the role of the prior and performs denoising. An efficient tone mapper network is also utilized to further improve the quality of the resulting images. The entire pipeline is then trained in an end-to-end fashion using perceptual loss. The proposed ISP has over$\mathbf{34}\times$fewer parameters in comparison to the state-of-the art deep ISPs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".