Innovative advancements in video processing are encapsulated in this research, focusing on a newly developed multi-picture architecture designed for optimizing the deinterlacing and demosaicing processes in video images. The study highlights a novel approach that integrates modified deformable convolution blocks and a unique residual efficient top-\(k\) self-attention (kSA) block, enabling enhanced spatio-temporal correlations and reconstruction accuracy. Key highlights of the study include:

• Development of a novel architecture combining multiple support pictures with a reference picture for precise data alignment and reconstruction.
• Use of modified deformable convolution and top-\(k\) self-attention blocks to improve the perceptual quality and performance metrics such as PSNR and SSIM.
• Comprehensive ablation studies showcasing the efficacy of the novel components in this architecture.

The presented architecture sets a new standard in video processing, pushing the boundaries of existing methods and encouraging further research in sophisticated video enhancement technologies. This study serves as a landmark in the field of engineered image processing, offering substantial benefits to real-world applications.