Dr. NAWAZ MehmoodXie, RongRongXieZhang, LiangLiangZhangAsfandyar, MalikMalikAsfandyarHussain, MuddsserMuddsserHussain2024-09-072024-09-072016Nawaz, M., Xie, R., Zhang, L., Asfandyar, M., & Hussain, M. (2016). Image super resolution by sparse linear regression and iterative back projection. In BMSB (Ed.). 2016 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB). 2016 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB), Nara, Japan. IEEE.978146739044697814673904532155-5052http://hdl.handle.net/20.500.11861/10470This paper presents a method which focus on the increase of visual quality of SR image reconstructed from input low resolution image. Similar to the framework as exploited in [1], a modified algorithm is developed which is based on sparse linear regression and iterative back projection. Different from the techniques used in [1] [6], a feature sign search algorithm [17] is used to find the relevant features of the regression function under a priori assumption. Furthermore, a modified Gaussian high pass filter is additionally used for the refinement of the initial reconstructed SR image through iterative back-projection technique to reduce visual artifacts. Experimental results conclude that this modified approach achieves better quality of reconstructed SR images than the other similar SR methods.enSuper-ResolutionLinear ProjectionSparse RegressionIterative ProjectionSparse Linear RegressionIterative Back ProjectionLow ResolutionImage ReconstructionHigh-Pass FilterGaussian FilterVisual QualityLow-Resolution ImagesLow-Resolution InputHigh-ResolutionStructural SimilarityHigh-Resolution ImagesVector-BasedReference MethodInverse ProblemImage PatchesSparse RepresentationSuper-Resolution ReconstructionBicubic InterpolationHigh-Definition VideoBicubicPSNR ValuesLasso AlgorithmRefinement ProcessSuper-Resolution ApproachesBlur EffectConference Paper10.1109/BMSB.2016.7521905