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

Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor