Fusion restoration is one of the most concise and practical methods for resolution reconstruction. To solve the existing problems related to fusion and restoration

this study proposes a new improved framework. The normalized convolution is improved and then used to implement the fusion step. The maximum a posteriori estimation is improved and then used to implement the fusion step. These improvements lead to the construction of a super-resolution reconstruction algorithm. In the fusion step of the proposed algorithm

the improved normalized convolution introduces a double applicability function. That adds a neighbor-intensity correlation. Then the improved normalized convolution introduces a new certainty function that mixes the Gaussian and Laplace certainty functions. In the restoration step of the proposed algorithm

the improved maximum a posteriori estimation introduces a feature-driven function. This function is obtained by mixing two constant prior models. The formulation of the feature-driven prior is completely determined using the statistics of the image feature. Several test images are synthetically degraded into low-resolution sequences with different disturbance levels. These sequences are then reconstructed using the proposed algorithm and other several algorithms for comparison. Results show that the proposed algorithm is superior to other algorithms in terms of visual effects and performance indexes. The fusion step in the proposed fusion restoration algorithm considers the spatial distance and intensity difference between neighboring pixels to efficiently restrain noise and outliers. The restoration step adopts the feature-driven prior that is determined by the image itself and not by experience. Therefore

the image is accurately characterized. The experimental results verify the effectiveness of the proposed algorithm.