Traditional image reconstruction methods in rapid dynamic diffuse optical tomography employ -norm–based regularization, which is known to remove the high-frequency components in the reconstructed images and make them appear smooth. The contrast recovery in these type of methods is typically dependent on the iterative nature of method employed, where the nonlinear iterative technique is known to perform better in comparison to linear techniques (noniterative) with a caveat that nonlinear techniques are computationally complex. Assuming that there is a linear dependency of solution between successive frames resulted in a linear inverse problem. This new framework with the combination of -norm–based regularization can provide better robustness to noise and provide better contrast recovery compared to conventional -based techniques. Moreover, it is shown that the proposed -based technique is computationally efficient compared to its counterpart (-based one). The proposed framework requires a reasonably close estimate of the actual solution for the initial frame, and any suboptimal estimate leads to erroneous reconstruction results for the subsequent frames.