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In this correspondence, a constrained least-squares multichannel image restoration approach is proposed, in which no prior knowledge of the noise variance at each channel or the degree of smoothness of the original image is required. The regularization functional for each channel is determined by incorporating both within-channel and cross-channel(More)
The determination of the regularization parameter is an important issue in regularized image restoration, since it controls the trade-off between fidelity to the data and smoothness of the solution. A number of approaches have been developed in determining this parameter. In this paper, a new paradigm is adopted, according to which the required prior(More)
In this paper, we propose a high-resolution image reconstruction algorithm considering inaccurate subpixel registration. A regularized iterative reconstruction algorithm is adopted to overcome the ill-posedness problem resulting from inaccurate subpixel registration. In particular, we use multichannel image reconstruction algorithms suitable for(More)
This paper introduces an iterative regularized approach t o increase the resolution of a video sequence. A m ultiple input smoothing convex functional is dened and used to obtain a globally optimal high resolution video sequence. A mathematical model of multiple inputs is described by using the point spread function between the original and bilinearly(More)