<p>In this article, we present a spectral dependent depth Layering (SDDL) model designed to enhance the rendering quality of new views in light field rendering (LFR). The SDDL model adeptly utilizes minimal depth information within the scene, effectively mitigating spectral aliasing resulting from undersampling during multiview capture, thereby significantly improving the rendering quality of novel views. We conducted a detailed analysis of the phenomena and principles behind spectral aliasing in the light field and derived the precise scene depth information required to eliminate spectral aliasing. The quantity of depth layers employed quantitatively represents the depth information utilized within the scene, with a greater number of depth layers reducing the need for multiview captures. In instances of undersampling, the incidence of spectral aliasing is reduced. Building on the use of depth layers for spectral aliasing elimination and considering the variation in scene depth with the surface of an object, we propose a strategy that uses the surface curvature of the object to select pertinent depth information. By strategically selecting depth information, we were able to effectively eradicate spectral aliasing in the light field, optimizing the rendering quality of new views. We validated the spectral elimination efficacy of the SDDL method by capturing multiview data from both simulated scenes and real-world scenes. Furthermore, we tested the performance of the SDDL in enhancing the rendering quality of multiple views through comparative experiments. The experimental results demonstrate that the SDDL method achieves effective quality optimization performance.</p>