Siyou Guo

Single-sensor imaging systems are widely deployed in portable devices including digital cameras, smartphones, and personal digital assistants (PDAs) for real-time image acquisition. While convolutional neural networks (CNNs) have demonstrated exceptional capabilities in various image processing tasks, their potential for demosaicking applications remains underexplored. This paper presents a demosaicking framework utilizing a Deep Residual Convolutional Neural Network (DRCNN) architecture.

Abstract Computation in biological neural circuits arises from the interplay of nonlinear temporal responses and spatially distributed dynamic network interactions. Replicating this richness in hardware has remained challenging, as most neuromorphic devices emulate only isolated neuron- or synapse-like functions.