This paper aims at recovering the hyperspectral image from its RGB counterpart. This highly challenging inverse problem has profoundly impactful applications, including hyperspectral imaging for metamaterial-driven miniaturized satellite. Popular inverse imaging theories include convex optimization (CO, wherein ADMM is a key optimizer) and deep learning (DL, wherein Adam plays a fundamental role); the former usually involves math-heavy optimization procedure, while the latter often requires time-consuming big data collection. We adopt the ADMM-Adam theory, recently investigated in the remote sensing literature for blending the advantages of CO and DL, in order to achieve outstanding hyperspectral signature reconstruction (HSR) without support from heavy math or big data. Simply speaking, a deep regularizer is devised to extract useful information embedded in the rough solution learned from small data. Then, such information is used to design a simple convex regularizer via Q-quadratic function for designing an effective HSR algorithm, whose effectiveness is experimentally illustrated.