Nowadays the result of infrared and visible image fusion has been utilized in significant applications like military, surveillance, remote sensing and medical imaging applications. Discrete wavelet transform based image fusion using unsharp masking is presented.DWT is used for decomposing input images (infrared, visible). Approximation and detailed coefficients are generated. For improving contrast unsharp masking has been applied on approximation coefficients. Then for merging approximation coefficients produced after unsharp masking average fusion rule is used. The rule that is used for merging detailed coefficients is max fusion rule. Finally, IDWT is used for generating a fused image. The result produced using the proposed fusion method is providing good contrast and also giving better performance results in reference to mean, entropy and standard deviation when compared with existing techniques.
Keywordsinfrared image, visible image, image fusion, DWT, unsharp masking 212| Panguluri and Mohan Period. Polytech.
Nowadays multimodal image fusion has been majorly utilized as an important processing tool in various image related applications. For capturing useful information different sensors have been developed. Mainly such sensors are infrared (IR) image sensor and visible (VI) image sensor. Fusing both these sensors provides better and accurate scene information. The major application areas where this fused image has been mostly used are military, surveillance, and remote sensing. For better identification of targets and to understand overall scene information, the fused image has to provide better contrast and more edge information. This paper introduces a novel multimodal image fusion method mainly for improving contrast and as well as edge information. Primary step of this algorithm is to resize source images. The 3×3 sharpen filter and morphology hat transform are applied separately on resized IR image and VI image. DWT transform has been used to produce "low-frequency" and "high-frequency" sub-bands. "Filters based mean-weighted fusion rule" and "Filters based max-weighted fusion rule" are newly introduced in this algorithm for combining "low-frequency" sub-bands and "high-frequency" sub-bands respectively. Fused image reconstruction is done with IDWT. Proposed method has outperformed and shown improved results in subjective manner and objectively than similar existing techniques.
The main principle of infrared (IR) image is that it captures thermal radiation of light. The objects that are captured in low light, fog, and snow conditions can be detected clearly in IR image. But the major drawback of IR image is that it provides poor resolution and low texture information. Due to that humans are unable to understand overall scene information present in IR image. Nowadays for the detection of objects in poor weather conditions with improved texture information, the result of visible (VI) and IR image fusion is used. It is mostly used in military, surveillance, and remote sensing applications. The efficient DWT based fusion algorithm for improving contrast and edge preservation is presented in this paper. First morphology hat transform is applied on source images for improving contrast. DWT on decomposition produces low frequency and high frequency sub-bands. A novel mean weighted fusion rule is introduced in this paper for fusing low frequency sub-bands. Its aim is to improve the visual quality of final fused image. The max fusion rule has used for fusing high frequency sub-bands to improve edge information. The final fused image is reconstructed by using IDWT. In this paper, the proposed fusion algorithm has produced improved results both subjectively and as well as objectively when compared to existing fusion methods.
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