GRENet: A 17k-parameters RAWtoRGB model pursuing visual artifacts mitigation

The RAWtoRGB reconstruction involves rendering a high-quality color image from raw data acquired by an image sensor. Many recent approaches, including deep learning models to their training paradigms, exhibit limitations in terms of algorithm complexity or image quality due to unwanted artifacts. Existing solutions often inadvertently introduce visual distortions that are often overlooked and underrepresented in evaluation metrics. Furthermore, the challenge of balancing hardware portability and image quality is often underrated. This paper therefore introduces the Grid Regularization Enhancement Network (GRENet), a lightweight RAWtoRGB model with only 16.7k parameters. GRENet demonstrates high-end performance, achieving state-of-the-art PSNR scores of small models (20.63 dB on ZRR and 24.76 dB on MAI2021). A key innovation is a novel supervised training loss, termed Grid loss, specifically designed to mitigate checkerboard-alike artifacts. This loss function not only improves reference metrics, with a gain of +0.25 dB on MAI2021 PSNR but also significantly enhances the visual quality of rendered images. To ensure a comprehensive evaluation, we assess GRENet with a wide range of carefully selected no reference image quality metrics along with the full reference ones. In addition and unlike most of the prior work, we conduct an in-depth visual rendering inspection, comparing GRENet’s reconstructions with those of state-of-the-art models. This analysis reveals a taxonomy of image artifacts commonly observed in reconstructions, providing valuable insights into the strengths and limitations of current approaches. Our comparisons demonstrate that GRENet exhibits a high reliability in mitigating visual distortions among existing methods.