000			01826nam a2200145 4500
005			20251028130120.0
008			251028b |||||||| |||| 00| 0 eng d
100			_aSumit Budhiraja
245			_aInfrared and Visible Image Fusion based on Sparse Representation and Weighted Least Square Optimization
300			_ap1504-1516
520			_aInfrared and visible image fusion brings complementary information from different images into a single image, improving its information processing capabilities. There is always a quest for maximum information extraction and subsequent transfer to fused images. In this paper, a scale-aware infrared and visible image fusion algorithm based on sparse representation (SR) and weighted least square (WLS) optimization is proposed. Firstly, a guided filter is employed to maximize information extraction from visible images. The source images are decomposed using a rolling guidance filter (RGF) based on the LoG filter and joint bilateral filter. RGF has both scale-aware and edge-preservation properties; it represents the image information at specific levels. The low-frequency coefficients are fused using sparse representation based on a Modified Prewitt-based clustered dictionary and high-frequency coefficients are fused using the max-absolute rule and WLS optimization. This optimization prevents the transfer of distortion and redundant information from the infrared image to the fused image. The subjective and objective performance evaluations show that the proposed algorithm could outperform other state-of-the-art techniques.
654			_aBilateral filter _aDictionary learning _aImage fusion _aRolling guidance filter _aSparse representation _aWeighted least square optimization
773	0		_080269 _9114212 _dNew Delhi IETE _tIETE Journal of Research _x0377-2063
942			_cJA
999			_c133083 _d133083