  Pixel binning based Quad sensors have emerged as a promising solution to
overcome the hardware limitations of compact cameras in low-light imaging.
However, binning results in lower spatial resolution and non-Bayer CFA
artifacts. To address these challenges, we propose a dual-head joint
remosaicing and denoising network (DJRD), which enables the conversion of noisy
Quad Bayer and standard noise-free Bayer pattern without any resolution loss.
DJRD includes a newly designed Quad Bayer remosaicing (QB-Re) block, integrated
denoising modules based on Swin-transformer and multi-scale wavelet transform.
The QB-Re block constructs the convolution kernel based on the CFA pattern to
achieve a periodic color distribution in the perceptual field, which is used to
extract exact spectral information and reduce color misalignment. The
integrated Swin-Transformer and multi-scale wavelet transform capture non-local
dependencies, frequency and location information to effectively reduce
practical noise. By identifying challenging patches utilizing Moire and zipper
detection metrics, we enable our model to concentrate on difficult patches
during the post-training phase, which enhances the model's performance in hard
cases. Our proposed model outperforms competing models by approximately 3dB,
without additional complexity in hardware or software.
